Suraj Jadhav


About Suraj Jadhav

Anjali is an engineer-turned-writer, editor, and team lead with extensive experience in blogs, guest posts, website content, social media content, CMS, & more.

llm software engineering
Languages, frameworks, tools, and trends

Revolutionizing Software Engineering with LLMs

Explore the benefits of AI-augmented software development with LLMs, including faster code generation, improved testing, and enhanced code quality.

In the rapidly evolving realm of software engineering, the integration of large language models (LLMs) is reshaping how businesses approach software development. Central to unlocking the full potential of LLMs is the fundamental skill of prompt engineering At its core, prompt engineering involves crafting input queries to extract precise and meaningful responses from LLMs. This pivotal skill empowers developers to elevate the accuracy and relevance of outputs, thereby optimizing the performance of AI applications. 

Erik Meijer, an engineering director at Facebook, compares the emergence of LLMs to the transition from manual labor to utilizing heavy machinery. This analogy highlights the huge boost in efficiency and potential LLMs can bring to the software development processes. With the introduction of AI-powered tools, such as GitHub Copilot and Duet AI, developers can understand and write complex code, generate test cases, design and publish APIs, identify and fix errors, and write code documentation. These features enhance developers’ productivity and allow them to focus on creative aspects of software development. 

For instance, developers at Turing, an AI-powered tech services company, experienced a remarkable 30% boost in productivity through the use of Duet AI. Another study highlighted a substantial improvement in task completion speed, revealing that developers leveraging GitHub Copilot finished tasks 55% faster than their counterparts without the tool.

LLMs and prompt engineering form a powerful duo, where precise prompts guide LLMs to deliver contextually relevant and informed outputs that transform software engineering tasks. Let’s explore how these innovative AI engineering tools, powered by LLMs, are shaping the landscape for AI engineers by offering efficiency and effectiveness in the ever-evolving world of artificial intelligence.

Let’s dive in!

Prompts and software engineering

The ability of LLMs to build upon a given prompt and produce context-appropriate outputs makes them exceptionally useful across applications. Unlike traditional models that require large amounts of manually labeled data, LLMs using prompt engineering can produce informed outputs from simple instructions. 

Prompts and software engineering

However, this innovation relies on creating and refining prompts to achieve the desired results. 

As a developer you must learn to write effective prompts to build, test, deploy, and manage complex software solutions. This skill will enhance your ability to leverage AI tools to their full potential for streamlining workflows and improving the quality of the final product. 

Here are a few tips to enhance the effectiveness of software engineering prompts:

  1. Be specific

Aim for clarity in your requests. The more detailed you are, the better the AI’s output.


Bad prompt: “Write some Python code for CSV files.”

Good prompt: “Create a Python function to read CSV files and list column names.”

  1. Direct instructions

Tell the AI exactly what to do.


Bad prompt: “Explain web APIs.”

Good prompt: “Describe the difference between POST and GET methods in web APIs.”

  1. Use examples

Provide examples to help the AI understand your goals.


Bad prompt: “Write a SQL query.”

Good prompt: “Write a SQL query for employees with salaries above $50,000, similar to this query for ages over 30: SELECT * FROM employees WHERE age > 30;”

  1. Refine your prompts

Begin with broad queries and narrow them down based on AI outputs.


Bad prompt : “How can I make a web app secure?”

Good prompt : “List methods to protect a web app from SQL injections and XSS.”

Strategic approaches for maximizing AI impact in development

Unlocking the full potential of LLM tools requires a strategic approach. Let’s explore essential recommendations to harness AI effectively to ensure adaptability, customization, collaboration, and continuous learning.

  1. Adapt and innovate: Stay agile by continuously exploring and experimenting with the latest AI tools and technologies. This proactive approach guarantees the ability to leverage the most effective solutions that align with evolving project needs and industry trends
  2. Focus on customization: Tailor your solutions to the distinct challenges and goals of each project. By fine-tuning LLMs and integrating specialized tools, this customized approach yields significant benefits that enhance efficiency, improve accuracy, and foster innovation.
  3. Enhance collaborative efforts: DuetAI and similar tools underscore the significance of collaboration between AI technologies and human expertise. Look at AI tools as your partners in the development process to both enhance productivity and spark creativity.

Integrating LLMs in software engineering

Key areas where LLM integration accelerates software development include:

Concept and planning: LLMs enhance brainstorming, requirements collection, and project scoping, turning ideas into detailed plans. 

Tools: ChatGPT, Bing Chat, Bard,

Design and prototyping: LLMs shorten the design cycle, providing instant prototypes and visual presentations.

Tools: Midjourney, Stable Diffusion, RunwayML, Synthesia.

Code generation and completion: LLMs automate coding tasks, improving efficiency and productivity.

Tools: GitHub Copilot, CodiumAI, Tabnine, DuetAI.

Code analysis and optimization: LLMs transform code examination by suggesting enhancements for performance and scalability.

Tools: p0, DuetAI, CodeRabbit, CodeQL, PR-Agent.

Test coverage and software testing: Generative AI tools, powered by LLMs, revolutionize software testing by automating the creation of unit, functional, and security tests through natural language prompts, providing alerts for potential code vulnerabilities and enhancing overall software quality.

Tools: Snyk, p0, TestGen-LLM.

Software architecture and documentation: LLMs aid in software design and generate comprehensive documentation.

Tools: Claude, ChatGPT, Cursor IDE.

Programming language translation: LLMs modernize legacy systems by translating code efficiently. 

Tools: Gemini, OpenAI Codex, CodeBERT.

Bad prompt : “How can I make a web app secure?”

Good prompt : “List methods to protect a web app from SQL injections and XSS.”

Let’s delve deeper into their influence on development and coding.

LLMs in development and coding

Code generation: Creating code snippets in different programming languages according to specific needs.

Code review and optimization: Examining code for potential enhancements, optimization possibilities, and adherence to coding standards.

Bug fixing: Detecting bugs in code snippets and proposing solutions.

Documentation generation: Automatically producing documentation for code bases, including comments and README files.

Code explanation: Simplifying complex code logic or documenting the functionality of code blocks.

Learning new technologies: Offering explanations, examples, and tutorials for new programming languages, frameworks, or libraries.

Despite their impressive capabilities, LLMs have limitations that developers should be aware of. These can include difficulties in understanding context, generating misinformation, and raising ethical concerns.

Below we have outlined some of the limitations faced by current LLMs in code generation tasks, along with corresponding workarounds for developers to overcome these challenges.

Context understanding

Limitation: Sometimes, Large LLMs may not fully understand the context of a coding task, leading to mistakes in the code they generate.

Workaround: Provide detailed prompts with clear examples and refine based on the initial outputs.

Language support

Limitation: Certain LLMs might not work well with some programming languages or might be better at others.

Workaround: Choose LLMs with language specialization. You can also explore multilanguage models or consider manual translation for unsupported languages.

Complexity and scale

Limitation: LLMs might struggle with big or very complicated projects because they have limits on what they can handle.

Workaround: Decompose tasks, use modular design principles, combine LLM-generated code with handcrafted code, and leverage external libraries.

Syntax and logic

Limitation: LLMs might create code with mistakes like typos or incorrect instructions, especially for complex tasks.

Workaround: Review and validate code manually, utilize linting tools, and consider pair programming for quality assurance.

Domain-specific knowledge

Limitation: LLMs might not know everything about specific topics like specialized techniques or industry rules.

Workaround: Domain-specific knowledge: LLMs might not know everything about specific topics like specialized techniques or industry rules.

Ethical concerns

Limitation: There could be worries about the fairness of the code produced by LLMs or how it might be used in the wrong way.

Workaround:Implement ethical guidelines and considerations when using LLMs, regularly assess for biases, and prioritize transparency and fairness in outputs.

AI tools in the software development life cycle

AI tools in the software development life cycle

From concept inception to deployment and maintenance, AI-driven solutions can streamline processes, enhance productivity, and drive innovation. Let’s explore the diverse array of AI tools and their impactful contributions across the software development life cycle.

Concept and planning

  • Chatbots (ChatGPT, Bing Chat, Bard, Use for brainstorming, gathering requirements, and initial project scoping.

Source: Suraj Jadhav

  • Search engines (Perplexity AI,, Conduct preliminary research to validate ideas and explore existing solutions.

Design and prototyping

  • Image generation (Midjourney, Stable Diffusion): Quickly create design mock-ups and UI/UX prototypes without extensive graphic design skills.

Source: Suraj Jadhav

  • Video generation (RunwayML, Synthesia): Produce demo videos and visual presentations to communicate design concepts.


  • Coding assistants (GitHub Copilot, CodiumAI, Tabnine, DuetAI): Automate code writing, suggest optimal coding practices, and autocomplete codes.

Source: Suraj Jadhav

  • Cursor IDE: Integrated AI chat for real-time coding assistance that identifies potential bugs, suggests fixes, and creates documentation.

Code review and quality assurance

  • PR agent: Automate code reviews to ensure adherence to best practices and identify potential issues.


  • Cursor IDE: Generate documentation by chatting with the code.
  • Meeting assistants (, Automatically transcribe meetings and generate documentation for team updates and decision logs.

Enhanced communication tools

  • AI writing assistants (Grammarly, Jasper): Use these tools to ensure emails are clear, concise, and professionally written. They can help with grammar, tone, and style to make your communication more effective.    

Source:Suraj Jadhav

  • Email drafting tools (Compose AI, OthersideAI): Automate email drafting to save time and ensure consistency in communication. These tools can generate email content based on brief inputs, making it easier to maintain regular correspondence with stakeholders.   

LLMs at Turing: Elevate your coding experience

The symbiosis between precise prompts and cutting-edge LLM tools has significantly elevated developer productivity, allowing developers to focus on creativity. Beyond prompt engineering, strategic approaches for maximizing AI impact underscore the importance of adaptation, customization, collaborative efforts, and a commitment to continuous learning. 

  1. As an AI engineer, your role extends beyond acquiring knowledge of large language models; you’re a pivotal force in the world of LLMs. We invite you to be part of Turing’s LLM journey, where we promote healthy challenges, nurture growth, and empower our community to excel in the dynamic AI landscape. Apply now and be part of a community that thrives on innovation and exploration. Your journey with LLMs starts here!


What are LLMs? Why are they used in software engineering?

LLMs are advanced AI tools designed to understand, generate, and work with humanlike language. Their integration into software development revolutionizes the way businesses build and manage applications by enhancing code generation, streamlining the software development life cycle, and enabling developers to focus more on creative problem-solving and less on repetitive coding tasks.

Are there any challenges associated with using LLMs in software engineering?

While LLMs offer tremendous benefits, their integration with software engineering processes comes with challenges. These include managing the accuracy of generated outputs, ensuring the AI’s solutions are contextually relevant, and addressing ethical considerations like data privacy and AI bias. Additionally, developers must be skilled in prompt engineering to communicate effectively with LLMs, and organizations must avoid overlooking the importance of human oversight.

How can developers leverage LLMs in their day-to-day work?

Developers can elevate their daily work by integrating LLMs into tasks like code generation, completion, analysis, and optimization. These models, equipped with advanced language understanding, significantly expedite software development processes by providing efficient solutions for various coding challenges.

What advancements are being made in the field of LLM research for software engineering?

Recent strides in LLM research for software engineering include refined prompt engineering techniques, improved code generation and completion capabilities, enhanced code analysis and optimization features, and the integration of LLMs in diverse stages of the software development life cycle.

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By , , Mar 8, 2024
Progressive Web Apps vs Native Apps What Should You Pick
Software Comparisons

Progressive Web Apps vs Native Apps: What Should You Pick?

Progressive Web Apps vs Native Apps: PWAs are apps that work seamlessly on any device, regardless of the platform, be it mobile, desktop, or tablet. Native.apps

The progressive web apps vs native apps battle intrigues many developers and businesses. So, will progressive web apps (PWAs) replace native apps in the future? What is the difference between PWA and native apps? What are the strengths and weaknesses of PWAs and native apps? Keep reading this blog post to find out. 

According to a report by Statista, smartphone subscriptions worldwide today surpass six billion. This number is predicted to grow further by several hundred million in the next few years. This creates an exciting opportunity for businesses to leverage the power of mobile apps to offer seamless user experiences. However, the last few years have seen rising support for progressive web apps (PWAs), and companies now face a pressing question: should they build a PWA or a native app? 

Also, read: React Native vs Swift: iOS Mobile App Development

Let’s explore the key differences between progressive web apps (PWAs) and native apps and determine which is better in 2024. Let’s get started!

Progressive web apps vs Native apps: What are the differences between PWAs and native apps? 

PWAs are web apps that can work seamlessly on any device, regardless of the platform, be it mobile, desktop, or tablet. PWAs offer a native app-like experience with the convenience of a website. They leverage modern web capabilities like service workers, and web app manifests to deliver a seamless user experience. PWAs can be easily installed on a user’s home screen, allowing them to access the app quickly without the need to go through the app store.

Native apps, on the other hand, are applications that are built specifically for a particular platform, such as iOS or Android. Native apps are installed on a user’s device through an app store, designed to take advantage of the device’s hardware and software capabilities. Native apps are built using specific programming languages and tools native to the platform, making them highly optimized for the platform they run on.

Also, read What Is Best for App Development: Flutter, Dart Language or Something Else?

What are some PWA and native app examples?

Let’s take a look at them below. 

  • Twitter Lite: Twitter launched its PWA in 2017 and offered its users web push notifications, an “Add to Homescreen” prompt, and temporary offline browsing. Twitter achieved a 75 percent increase in Tweets sent, a 20 percent reduction in the bounce rate, and a 65 percent increase in pages per session. 
  • Flipkart: In 2015, Flipkart launched its PWA and reported a 70 percent increase in conversion rate, a 40 percent increase in re-engagement rate, and increased time spent on site by 3x.
  • Starbucks: In 2017, Starbucks launched its PWA and reported a 23 percent increase in order diversity and a 2x increase in their daily active users.
  • Pinterest: After launching its PWA, Pinterest noticed a 60 percent increase in core engagements and a 44 percent increase in ad revenue rate.

Let’s look at some successful examples of native apps:

  • Instagram: With over 2.3 billion active monthly users, Instagram is the second most-downloaded app globally. This native app allows users to share photos and videos with friends and followers.
  • Uber: Uber is a ride-hailing app that allows users to book rides on demand. The annual revenue generated by Uber in 2022 was $31.8 billion, with an 82.62% increase from the previous year.
  • Spotify: Spotify is the world’s leading music streaming application, with over 205 million premium subscribers worldwide. The app allows users to listen to their favorite songs and discover new music. 
  • Headspace: With 1.32 million downloads worldwide in the fourth quarter of 2022, Headspace is a meditation and mindfulness app that helps users reduce stress and anxiety and is popular for its recognizable animations.

Read, also: Top 3 Databases for iOS Mobile Applications

Progressive web apps pros and cons

Here are the main pros and cons of PWAs you need to keep in mind. 


  • Cost-effective: Progressive web apps require less development time and are easier to maintain, resulting in lower development costs than native apps.
  • Fast and responsive: They offer quick loading and a smooth user experience due to the service worker technology that enables offline usage and caching.
  • Cross-platform compatibility: PWAs are built using web standards and are accessible on any device or browser, including desktops, mobile, and tablets.
  • Lower storage requirements: They take up less space on a device than native apps.
  • SEO-friendly: PWA can be indexed by search engines like any other website, which means better visibility on search engine result pages (SERPs).
  • Secure: PWAs are secure as they follow HTTPS protocols and transmit encrypted information.


  • Limited access to native device features: Progressive web apps may be unable to access all of the features available on a user’s device, including push notifications, a camera, and an accelerometer.
  • Limited discoverability: PWA is not widely available on app stores like Google Play or Apple App Store.
  • Security risks: PWAs are vulnerable to cyberattacks and may require additional security measures to protect user data.
  • Limited user engagement: Progressive web apps may not have the same engagement as native apps, such as social sharing or user-generated content.

Native apps pros and cons 

Just like PWAs, native apps come with their own pros and cons. They’re as follows. 


  • Fast & efficient: Native apps are faster and more efficient compared to web apps as they are designed specifically for the platform they are running on.
  • Better user experience: They offer a better user experience as they can use the features and hardware of the device to their fullest potential.
  • High performance: Native apps can be accessed offline, providing better performance and stability.
  • Offline mode: They can use push notifications and provide offline data storage.
  • Accessible: Native apps can be downloaded from app stores, making it easy for users to discover and install the app.


  • Slow time to market (TTM):  Native apps require more resources and time to develop, requiring a separate version for each platform.
  • Frequent upgrades: Native apps must be updated and maintained separately for each platform.
  • Higher cost: The development cost of native apps is higher than web apps.
  • Lengthy downloading process: Native apps require users to download and install them, which can be a barrier to entry for some users.

Progressive web apps: Use cases

Let’s quickly look at the use cases of PWAs below.

  • Offline Mode: Progressive web apps work offline, making them an ideal choice for users who are traveling or have limited connectivity. The PWA can save the user’s data and allow them to continue using the app without a stable internet connection.
  • News and Media: Progressive web apps are an excellent choice for news and media companies that want to offer a fast, responsive mobile experience, such as Forbes, Medium, etc.
  • Travel and Hospitality: PWAs can help travel companies offer a fast and reliable booking experience by providing real-time updates on travel information, such as flight status, gate changes, and delays. Some well-known examples include Trivago, Make My Trip, Ola, etc.
  • Banking and Finance: PWAs are ideal for banking and finance companies as they can offer users the ability to access their accounts, check balances, and pay bills. Examples include payment systems such as PayPal and Stripe.
  • Health and Fitness: Progressive web apps can help health and fitness companies offer an interactive and personalized experience. They can provide users with workout plans, track their progress, and deliver customized tips and advice—for example, Fitbit, Headspace, Nike Training Club, etc.

Native apps: Use cases

And here are the use cases of native apps.

  • E-commerce: Native apps are often used for e-commerce businesses, allowing customers to purchase directly from their mobile devices—for example, Amazon, eBay, etc.
  • Social media: Popular social media apps like Facebook, Instagram, and Twitter are all native apps, allowing users to easily access and share content on their mobile devices.
  • Gaming: Native apps are also commonly used for gaming, with popular titles like Candy Crush, Clash of Clans, and Angry Birds.
  • Education: Native apps are also used in education, with many schools and universities providing mobile apps that allow students to access course materials, communicate with instructors, and more. Some popular apps include Duolingo, Minecraft Education Edition, Photomath, etc.
  • Productivity: Native apps can also be used for increasing productivity, with popular apps like Microsoft Office, Evernote, and Trello.
  • Entertainment: Native apps are often used for entertainment purposes, with streaming services like Netflix, Hulu, and Disney+.

Progressive web apps vs Native apps: Which is better in 2024?

The answer to this question depends on various factors, such as your business goals, the target audience, and the required features. However, let’s look at the key differences between PWAs and native apps to determine which is better in 2024.

  • Development cost and time
    One of the primary reasons why businesses are turning towards PWAs is their development cost and time. Building a PWA is generally less expensive than a native app, and the development time is relatively shorter. PWAs are built using web technologies that are already familiar to developers and can be easily deployed to multiple platforms.
  • User experience
    Native apps offer a more polished and refined user experience than PWAs. Native apps are designed to take advantage of the hardware and software capabilities of the device, providing a seamless and responsive experience to users. On the other hand, PWAs are limited to the capabilities of the web browser and may not offer the same level of responsiveness as native apps.
  • Accessibility
    PWAs are more accessible than native apps, as they can be accessed on any device with a web browser. Native apps, on the other hand, are limited to a particular platform and require users to download and install the app before they can use it. This means that PWAs can reach a wider audience than native apps.
  • Maintenance and updates
    Maintaining and updating a PWA is generally easier than a native app. With PWAs, updates can be made on the server side, and users can access the latest version of the app without needing to download and install anything. On the other hand, native apps require users to download and install updates, which can be cumbersome.


It takes careful planning and assessment of project requirements, resources, and timelines to choose the best technology for building your next mobile application. Both PWAs and native apps have their own pros and cons. You can take your pick based on various factors like product requirements, business goals, and target audience. If a business wants to reach a wider audience and save development costs and time, then PWAs are a better choice. On the other hand, if a business wants to provide a more refined and polished user experience and take advantage of the hardware and software capabilities of the device, native apps are what they need. 

If you’re looking to hire pre-vetted, skilled app developers, Turing’s got you covered. Hire from a talent pool of 50k+ developers and engineers ready to work in your timezone. Visit the Hire Developers page to know more.

If you’re a developer looking for top remote US software jobs, you’re at the right place. Head over to the Apply for Jobs page for more information.

Join a network of the world's best developers and get long-term remote software jobs with better compensation and career growth.

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By Mar 17, 2023
Engineering competency matrix
For Employers

Engineering Competency Matrix: Here’s Why You Need It

An engineering competency matrix helps companies define & access their employees’ skills. This post shares 6 compelling reasons why every company needs one.

In a world where technology influences everything, it becomes essential for companies to sustain their competitiveness. An engineering competency matrix or a skill matrix is a tool that helps companies define, visualize, and access their employees’ skills and competencies. The competency matrix also helps in determining engineering competencies and managing them effectively.

This post discusses the benefits of developing a robust engineering competency matrix for your organization. 

Benefits of building an engineering competency matrix

Identifies and eliminates skill gaps

Skill matrices help organizations understand an employees’ current skills and competencies and identify the skills required for executing a new project or improving existing ones.

Increases organizational performance and productivity

When managers know their teams’ capabilities, it becomes easier to allocate resources and delegate responsibilities effectively. And thus, the skill matrix enhances the overall productivity of the organization.

Augments the hiring process 

Recruitment teams can leverage the competency assessment to structure their hiring processes, write better job descriptions, set expectations, and make unbiased decisions. 

An engineering competency matrix also comes in handy while scaling the organization. The matrix helps employers recruit the best talent with the required experience and skills. 

Enhances training and development programs

With the knowledge of employees’ strengths and weaknesses, companies can plan and execute various training and development programs. Such initiatives help employees upskill and contribute effectively to the organization while accelerating their careers.

Initiates team restructuring

A skill matrix provides insights into the competencies and deficiencies of an employee. This knowledge assists the managers in restructuring the team and improving performance.

Promotes reward management

Team leads can utilize engineering matrices to assess their team members’ strengths, evaluate their performance, and reward their dedication and hard work. Reward management is crucial to ensure a company’s success. What’s more, this system motivates individuals to perform better and step up their career ladder. 

Build an engineering team competency matrix with these simple steps

Quantifying a person’s skills is challenging. However, it is possible to build an effective engineering competency matrix with proper planning and execution. A company can implement cost-effective practices to stay ahead of the competition with a strategic approach to analyzing the strengths and weaknesses of its workforce.

Here’s how you can build an effective engineering competency matrix:

  • Delegate responsibility

    The first and the most important step in the process is to assign a dedicated resource to take the responsibility of building the skill matrix from the ground up. This person should be aligned with the company’s objectives and values to help create and execute an effective assessment plan.
  • Define the objectives and team values

    Be clear about the goal behind creating a skill matrix. Once the goal is clear, define the company or team values. Setting the priorities right helps in building a cohesive work environment. Your goal could be one of the following:
    • Evaluate employee performance
    • Define skill gaps
    • Improve hiring processes
    • Build or restructure the team (if required)
    • Start a new project
  • List down the core competencies.

    Develop a list of the core engineering skills necessary to perform the job and categorize them into two groups: hard skills and soft skills.
    Hard skills can include prototyping, coding, proficiency with programming languages (Python, Java, C++, etc.), database management, code debugging, testing, QA, and familiarity with related libraries, tools, and frameworks. Critical soft skills can include teamwork, communication, conflict resolution, problem-solving, etc.
    Create a task to regularly review and update this list to identify new skills and remove obsolete technologies as per the business requirements.
  • Define the level of competencies

    The next step is to define the range of different levels of competencies for each of the core skills. This step aims to identify an employees’ existing skill competencies and devise a training plan to enhance their performance.
    For example, if the core skill is Python, different levels of competencies can be identified as follows:
    • Expert or Advanced
    • Intermediate
    • Beginner
      Engineering competency matrix (1)
  • Take feedback and iterate

    Once the first version of the engineering competency matrix is ready, send it for review and feedback. Remember, the skill matrix will influence cross-functional teams and overall business decisions. And so, feedback from the stakeholders will help improve the matrix and deliver expected results.

    Pro tip: Make these iterations a part of the company’s quarterly or annual planning process to meet business objectives.

To sum up, an engineering competency matrix can help organizations in multiple ways

Engineering leaders and hiring managers should harness human capital by assessing skills and competencies. They must know when to redesign the matrix to sustain competitiveness and drive growth within the organization. Last but not least, leaders and managers should use a competency matrix to make informed decisions about training and development processes and determine the company’s hiring needs.

If you have identified skill gaps in your engineering competency matrix and are looking to hire talented software developers, try is a data-science-driven deep jobs platform helping companies spin up their engineering teams in the cloud at the push of a button. With Turing, companies can hire pre-vetted, Silicon Valley-caliber remote software talent across 100+ skills in 3-5 days. Turing’s Intelligent Talent Cloud uses AI to source, vet, match, and manage over a million developers worldwide.

Tell us the skills you need and we'll find the best developer for you in days, not weeks.

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By Apr 4, 2022
Automation Testing Trends to Lookout for in 2022
Custom Engineering

The Top Eight Automation Testing Trends to Lookout for in 2024

With rapid advancements in modern technologies, the demand for automation testing is at an all-time high. Here are the top automation testing trends for 2023.

Unlike in the past, software testing is not just about identifying and troubleshooting application errors. Earlier, the goal of such testing was to ensure high performance and quality in the shipped products. Nowadays, companies need to optimize their time-to-market metrics, automate manual tasks, maximize resource utilization, and scale their team’s productivity to deliver an exceptional user experience. Automation testing helps companies achieve their business goals by speeding up the product delivery process while maintaining quality.

With rapid advancements in modern technologies such as IoT, Artificial Intelligence, Machine Learning, etc., the demand for automation testing is at an all-time high. 

According to MarketsandMarkets, “The Automation testing market size is expected to grow to USD 49.9 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 19.2% during the forecast period.”

This post will discuss the top eight automation testing trends for 2024. 

Let’s get started!

  1. Hyperautomation testing

    One of the top trends in the automation testing market, hyperautomation, combines the power of Artificial Intelligence, Machine Learning, and automation technologies. It augments human capabilities and offers flexible, scalable, and faster product development.
    QA teams can now deploy digital testers (supported by various test pipelines) to reduce human intervention and automate the entire testing process. In addition, companies can leverage advanced technologies such as Business Process Management (BPM), Robotic Process Automation (RPA), AI, and ML to achieve their business goals.
  2. Cloud-based cross-browser testing

    Gone are the days when developers wrote different codes for different devices and browsers. With advancements in technology supporting cross-platform development, automating the testing processes across multiple browsers and testing environments has become crucial.
    Companies nowadays adopt cloud-based cross-browser testing methods to reduce testing infrastructure costs and enhance scalability. This process enables QA teams to test their web apps seamlessly across various device-OS-browser combinations and deliver high-quality products.
  3. Non-functional testing

    Customer satisfaction is one of the crucial metrics to measure a software product’s success. Therefore, it becomes critical to perform non-functional testing to increase the efficiency and usability of the product and reduce production risks and costs. Some of the main categories of non-functional testing are:
    • Accessibility testing

    • Security testing

    • Performance testing

    • Infrastructure testing

    • Stress testing

    • Load testing

    • Disaster recovery testing

  4. Automated regression testing

    Automated regression testing ensures that recent code modifications do not break the existing functionalities and provide a safety net to find regression bugs. This feature saves time and helps deliver efficient solutions with enhanced scalability and speed.
    ROI and effective bug detection are the key advantages of automated regression testing. Automated regression testing tools include Selenium, TimeShiftX, IBM Rational Functional Tester, SilkTest, TestComplete, Ranorex, etc.
  5. API testing

    It is common to use third-party libraries and APIs while developing web applications and software solutions. However, with thousands of applications using the same API, testers and developers must test these APIs thoroughly to ensure the error-free performance of applications.
    Automating the API testing process helps QA teams quickly identify and troubleshoot bugs. It also allows seamless integration with third-party API services.
  6. DevTestOps

    Trends in automation testing

    Trends in automation testing: DevTestOps

    DevTestOps is the latest trend in the software testing domain and will continue to trend in 2024. While DevOps helps organizations deploy system changes efficiently and quickly, DevTestOps enhances deployment quality through continuous and automated testing.
    QA teams can use CI/CD pipelines with AI to boost the automated testing and product delivery processes.
    The main advantage of DevTestOps is the identification of bugs before the final release of the software, which improves the customer experience.
  7. Codeless automation

    Codeless automation is the next big thing in the world of automated testing. Testers can now save a lot of time by automating the process of writing and running test scripts.
    According to Future Marketing Insights, “The demand for codeless testing will rise at 15.5% CAGR between 2021 and 2031.”
    Codeless automated testing eliminates the need to write thousands of code lines, regardless of the application’s type and size. This feature maximizes testing efficiency, reliability, and stability across the SDLC process. Selenium is one of the most popular testing tools that offer codeless automation, allowing developers to focus more on building innovative products. 
  8. Mobile automation testing

    With the ever-increasing usage of mobile devices and the internet, mobile automation testing will continue to be a top automated testing trend in 2024.
    According to Market Research Future (MRFR), “The global mobile application testing services market is expected to reach a market value of USD 13585.73 Million by 2026, growing at a CAGR of 20.3%.”
    By automating mobile application testing, QA teams can improve regression test cases, boost testing productivity, reduce product delivery time, and ensure higher customer satisfaction.

Here is a list of the top eight automation testing tools to use in 2024:

  1. Selenium

  2. IBM Rational Functional Tester (RFT)

  3. TestComplete

  4. Katalon Studio

  5. Appium

  6. Cucumber

  7. SoapUI

  8. WorkSoft

Wrapping up

Automation testing helps organizations significantly reduce product delivery time and accelerate progress. It also assists development and testing teams to improve software quality by providing comprehensive error reports. 

Large-scale enterprises and fast-growing start-ups can leverage automation testing services to ensure the smooth functioning of their business operations, reduce operational costs, and improve customer experience. 

Undoubtedly, automation testing is the future of software testing. Adopting the latest trends will prove to be an effective strategy for businesses to gain a competitive edge in the industry.

Turing is a data-science-driven deep jobs platform helping companies spin up their engineering teams in the cloud at the push of a button. Turing’s Intelligent Talent Cloud uses AI to source, vet, match, and manage over a million developers worldwide. With Turing, companies can hire pre-vetted, Silicon Valley-caliber remote software talent across 100+ skills in 3-5 days. 

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By Jan 21, 2022
Recruitment metrics
For Employers

Top Eight Recruitment Metrics You Should Track in 2022

HR professionals can leverage these recruitment metrics and data analytics to attract talent and devise strategies to increase employee retention rates

With the ever-increasing demand for hiring the best talent faster and efficiently, HR managers and recruitment teams are shifting towards data-driven recruitment for optimizing their hiring process. Recruitment metrics are an integral part of data-driven recruitment and help evaluate the health and effectiveness of the hiring process. 

HR professionals can leverage these metrics and data analytics to attract talent and devise strategies to increase employee retention rates. 

Recruitment metrics also assist talent acquisition teams in increasing business value and enabling strategic workforce planning.

Here are the top eight recruitment metrics you should track to make informed hiring decisions in this post.

Let’s get started!

Time to fill

This is one of the crucial recruitment metrics that allows HR managers to see how long it will take their team to fill open positions or replace churned employees. 

Time to fill refers to the number of days it takes to source and hire a new candidate. 

Usually, it is the amount of time between approving a job requisition and the candidate accepting your offer. 

Several factors influence this recruitment metric, including supply, demand, industry requirements, or the speed at which your talent acquisition teams perform

Recruitment metrics

Recruitment metrics: Time to Fill vs. Time to Hire

Time to hire

‘Time to fill’ is often confused with ‘time to hire.’ However, they are entirely different recruitment metrics. 

The time to hire metric tracks the speed at which a candidate is screened, evaluated, interviewed, and hired for a position. 

It indicates the efficiency of the hiring process and the overall candidate experience. 

This recruiting metric depends on the role type, industry, and your company’s hiring process.

Quality of hire

This is one of the most important recruitment metrics that indicates a new candidate’s value to your company’s long-term success. 

As per LinkedIn’s Global Recruiting Trends Report, the quality of hire metric continues to be the most valuable KPI. This metric considers the following factors:

Sub recruitment metrics: Employee engagement

This metric describes the relationship between an organization and its employees qualitatively and quantitatively.

Sub recruitment metrics: Time-to-productivity

This measures the new hire’s time to full productivity. Gage fresh candidates on how quickly they attain certain productivity levels. 

Sub recruitment metrics: Job performance indicators

These indicators are the reporting manager’s performance reviews for a given employee. 

Sub recruitment metrics: Cultural fit

This metric is measured by 360-degree ratings of the new employees by their colleagues and managers.

According to The U.S. Department of Labor, a bad hire can cost your company 30% of the employee’s potential first-year earnings. Though measuring the quality of new employees can be subjective, doing so is critical for your organization’s success. 

Source of hire

This recruitment metric indicates where your new hires come from. 

This indicator can help identify which sources and channels bring in the most talented candidates for your open positions and drive significant business ROI. 

You can analyze channels such as social media advertising, referrals, recruitment agencies, job listing platforms, paid advertising platforms, and your careers page to find the most successful one. 

Applicants to hire ratio

This is a useful recruitment metric representing the number of applicants applying for a role to the number of candidates hired for that position. 

It helps recruiters understand the intensity of the hiring process and decide if they should implement additional hiring criteria to vet candidates.  

When the applicants-to-hire ratio is consistently higher, e.g., 100 applicants for one position (100:1), recruiters can use pre-employment tests and employ data-driven recruitment processes to save time and resources while hiring the most qualified candidates.

Cost per hire

This is an important hiring metric that indicates the efficiency of the recruiting process and helps evaluate the ROI. 

Cost per hire is the average amount of money you spend for recruiting an employee. This measure comes in handy while tracking your recruiting budget.

CPH= (Internal Recruiting Costs + External Recruiting Costs) / Total number of hires

CPH includes recruitment costs, onboarding costs, travel costs, equipment, administrative costs and benefits, referral programs, job board fees, paid advertising costs, agency fees, among others.

Attrition rate

Attrition rate or churn rate is the rate at which employees leave your organization in any given period. 

Attrition rate = Number of employees who have left the company / Average number of employees over a given period.

High attrition rates are a matter of concern for recruiters as replacing top talent can be costly in terms of money and resources required to onboard new candidates. 

Companies need to evaluate this metric to understand their employee satisfaction levels and shape their employee retention strategies accordingly.

Candidate job satisfaction

Recruitment metrics

Recruitment metrics: Job Satisfaction

This is yet another valuable recruitment metric that assesses the candidate’s experience with the recruiting process and their satisfaction in their current role in the organization. 

One of the key indicators of candidate job satisfaction is the Candidate NPS (Net Promoter Score.)

This indicator helps HR professionals to understand how likely newly hired candidates are to recommend their organization to a friend/colleague based on their experiences.  

Assessing a candidate’s experience is the key to acquiring top talent by understanding if the job description matches the new hires’ expectations and how well the recruiting team presents the job opportunity. 

So, why should you track recruitment metrics?

Undoubtedly, recruiting top talent is crucial to an organization’s success. 

It is high time that HR professionals start leveraging data and analytics to scale and optimize their recruitment processes to hire the most qualified candidates faster. 

Switching to data-driven recruitment can help recruiters reduce hiring costs and maximize efficiency gains. 

Monitoring recruitment metrics and using data can give you a competitive advantage and assist you in making well-informed hiring decisions that align with the business goals and ensure human capital and organizational growth. 

Turing is a deep jobs platform that uses AI to source, vet, match, and manage software developers from across the globe. We make the remote hiring journey easy and rewarding for both companies and developers. 

With Turing, companies can hire pre-vetted, Silicon Valley-caliber remote software talent across 100+ skills in 3-5 days. From Fortune 500 companies to fast-scaling startups, hundreds of organizations trust us to scale their offshore engineering teams effortlessly.

More than 200 firms, including well-known, new-age companies backed by top firms like Google Ventures, A16Z, Bloomberg, Khosla ventures, etc., have hired our pre-vetted developers.

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By Dec 9, 2021
Node.js vs Python
For Employers

Node.JS vs. Python: What Should You Choose?

This Node.js vs. Python post compares the two technologies in detail to help you make an informed decision and build high-performance apps for your next project.

The server-side or backend of an application is the core foundation of any project. It is this foundation that supports the client-side, user-facing elements of a web application. And thus, picking the right backend technology can be a gamechanger when building high-performance applications. This post compares two of the most sought-after backend technologies, Node.js vs Python, to help you make an informed decision while picking one for your project.

Let’s get started!

What is Node.JS? 

Node.js is an open-source, JavaScript runtime environment that runs on Chrome’s V8 engine and executes code outside the client’s web browser. 

It is a cross-platform and asynchronous event-driven environment designed to build fast and scalable network applications. Node JS developers make optimal use of this technology for server-side programming and performing non-blocking I/O operations. 

What is Python? 

Python is an interpreted, high-level, general-purpose programming language with dynamic semantics. It is widely used for backend development and follows an object-oriented approach. 

Features like dynamic binding, built-in data structures, dynamic typing, etc., make Python a perfect choice for RAD (Rapid Application Development). It is also used as a scripting or glue language to connect the existing components. 

Python developers are responsible for building the backend of the web, mobile, and desktop applications. 

Node.JS vs Python: Comparing the pros, cons, and use cases

Node.js vs Python

Node.js Pros and Cons

Node.js vs Python

Python Pros and Cons

Top five Node.JS use cases

  • Netflix and Trello use Node.js to build single-page web applications.

  • LinkedIn uses Node.js due to its performance efficiency and scalability.

  • PayPal prefers Node.js as it provides faster development, improves the average response time, and reduces the lines of code significantly.

  • Uber relies on Node.js for building its matching systems as it provides faster deployment and processes enormous amounts of data efficiently, thus meeting Uber’s business requirements.

  • eBay uses Node.js as it offers speed, streamlined development, and automation while handling I/O bound operations.

Top five Python use cases

  • Instagram uses Python for developing its backend and scaling service infrastructure. 

  • Google relies on Python as it offers simplicity, speed, and ease of deployment.

  • Spotify uses Python for performing data analysis, building backend services, and speeding up the development process.

  • Quora prefers Python due to its readability, ease of writing code, and availability of many frameworks such as Pylons, Django, etc.

  • Industrial Light and Magic (ILM) opt for Python as it offers easy interoperability with C++ and C and helps in building efficient production pipelines & extending existing applications.

Node.JS vs Python: Which one’s the best for your project?

Understanding the functional requirements is one of the most critical factors that determine the success of your project. 

Once those are clear, you can use these criteria to decide which backend technology fits your project best:

  1. Learning curve and syntax

    The syntax of Python is pretty straightforward. It requires fewer lines of code, making the code easy to debug and understand.
    Any developer with a strong technical background can quickly start working with Python. Node.js syntax is identical to JavaScript, and developers with previous experience in JavaScript often find it easy to understand Node.js. 
  2. Architecture

    Node.js is an event-driven runtime environment with the ability to perform non-blocking I/O operations. It can process thousands of concurrent requests without incurring thread context switching.
    These features make Node.js the best pick for real-time web apps like chatbots and gaming applications. On the other hand, Python has an architecture that’s synchronous and, therefore, is slow in performance and speed.
    But, for parallel processing and event-driven projects in Python, developers can use libraries like Ayncio and modules like CPython, respectively. 
  3. Libraries

    Pip is the default package handler for Python libraries and packages. Python has over 220K packages for various categories, including data science, image processing, calculations, etc.
    Node JS libraries and packages are handled by Node Package Manager or npm, with over 1.3 million well-documented and easy-to-use packages. 
  4. Speed and performance

    Node.js executes the code outside the client’s browser, and it also provides a non-blocking architecture, hence offering high speed and performance. In addition, Node.js enables caching fetched data which makes the apps responsive, reducing load time.
    In comparison to Node.js, Python is slower as it does not support multithreading. Therefore, Python won’t be a good choice if your project often needs to recall data from the web servers. 
  5. Scalability

    Python lags in scalability as it does not support multithreading and uses GIL (Global Interpreter Lock (GIL). Python is also an interpreted language, which makes it a slow programming language.
    Node.js offers high scalability as it supports multithreading and comes with a cluster module that can handle the full processing power of the machine. With Node.js, developers can scale their apps vertically and horizontally by adding more resources and new nodes to the system, respectively. 
  6. Error handing

    It is essential to have a robust error handling system to offer your customers a better user experience and write clean and reusable code.
    Python is better in error handling due to its clean, readable code and compact syntax. As a result, it takes less amount of time to troubleshoot and fix errors.
    Node.js can debug errors, but it becomes difficult to do so when multiple requests are processed concurrently to achieve high performance with multithreading.
  7. Extensibility

    It’s easy to extend both Python and Node.js. Developers can use Python frameworks such as Robot, CherryPy, Django, Flask, etc., and APIs to extend the language’s features and capabilities.
    The functionalities of Node.js can be extended using its built-in APIs, frameworks such as Meteor, Hapi, Fastify, etc., testing frameworks such as Jasmine, and more. 
  8. Community

    When it comes to finding peer support and online resources, Python and Node.js have large and active communities.
    Their community forums offer excellent opportunities to collaborate with developers from across the globe with different levels of experience and expertise. Skilled developers worldwide are free to contribute to Node.js and Python, including their packages.

Node.JS is suitable for projects like:

  • Messengers and chatbots

  • Complex single-page apps (SPA)

  • IoT implementations

  • Collaboration tools and systems

  • Data streaming applications

  • Microservices architecture

  • Online eCommerce and gaming platforms

Node.JS is not suitable for projects like:

  • Apps that require heavy computations

  • CRUD or HTML applications

  • Backend apps with relational databases

  • Multi-threaded programming projects

Python is suitable for projects like:

  • Progressive web applications (PWAs)

  • IoT projects

  • Data visualization & image processing

  • AI/ML and Neural Networks

  • Voice and facial recognition

  • Startups, Fintechs, Finance

  • Data Science and Engineering

Python is not suitable for projects like:

  • Mobile app development (Not supported by iOS and Android)

  • Game development (High memory consumption)

  • Projects that require interaction with complex legacy data

Node.JS vs Python: Conclusion

Choosing between Node.js and Python can be overwhelming, especially when developing a full-flagged product instead of a side project. However, both these languages have their own set of advantages and disadvantages.

And thus, the goal of the project and the developer’s competence are the two most essential factors that you should consider when deciding on a technology for the development process. 

Turing is a deep jobs platform that allows companies to source skilled and experienced engineers planet-wide. 

You’ll have access to a talent pool of the top 1% of 1M talented and experienced developers with strong technical and communication skills who can work according to your requirements. 

Additionally, Turing’s tried-and-tested vetting process evaluates developers to a Silicon Valley standard allowing you to have your pick of qualified software developers without sucking up all of your engineering team’s time.

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By Nov 24, 2021
The Basics of Web Application Penetration Testing
For Employers

The Basics of Web Application Penetration Testing

Web application penetration testing can help organizations achieve the highest system security. Here’s everything you need to know about penetration testing.

Due to the increase in the complexity of cyberattacks, companies are investing more resources than ever to secure their systems from reputational and financial losses. One of the most used security testing techniques is web application penetration testing, Pen Test or Pen Testing. 

Web Application Penetration Testing

Web Application Penetration Testing: Market Research

Web application penetration testing involves simulating cyberattacks against application systems (APIs, front-end servers, back-end servers) to identify exploitable vulnerabilities and access sensitive data. It helps companies verify their systems’ security, identify any vulnerabilities and their scope of the damage, and develop strategies to mitigate potential threats. 

Types of web application penetration testing

There are two major types of penetration testing for web applications:

  • Internal pen testing

    This type of testing focuses on the web applications hosted on the intranet within the organization. The goal is to identify any potential vulnerabilities within the corporate firewall by using invalid credentials to access the system and determining the possible damage and route of attacks. Some of the most common internal attacks are: 
    1. Simulation of Phishing attacks 
    2. Malicious employee attacks 
    3. Attacks using user privileges 
    4. Social engineering attacks
  • External pen testing

    This type of penetration testing focuses on external attacks on the web applications hosted on the internet. The testers (aka ethical hackers) simulate external attacks using the IP address of the target system. External pen testing involves testing the applications’ firewalls, IDS, DNS, and front-end & back-end servers.
    In addition to these, there are a few more approaches to pentest, such as blind testing, double-blind, and targeted testing.
    Web application penetration testing

    Web Application Penetration Testing: Importance

Steps of Web Application Penetration Testing:

  • Planning and reconnaissance

    This step involves defining the goals and objectives of the test process, gathering information (servers, networks, domain names, etc.), and choosing the tools and techniques for testing. Based on the type of interaction required with the target system, there are two types of reconnaissance:
    • Active Reconnaissance

      In this process, the tester directly probes the target system to gather information. Some of the approaches for active reconnaissance are:
      • Shodan network scanner 
      • Fingerprinting the web application 
      • DNS zone transfer 
      • DNS forward and reverse lookup
    • Passive Reconnaissance

      The tester gathers the information available on the internet without having a direct interaction with the target system.
      Some of the popular tools used for web application penetration testing are listed below:
      • W3af
      • Veracode
      • Burp Suite
      • SQLMap
      • ZAP
      • Metasploit
      • Acunetix
      • Vega
      • Skipfish
      • Ratproxy
      • NetSparker
      • Watcher
  • Scanning and exploitation

    Once the testers have all the required information at their disposal, they can simulate cyberattacks on the web applications and discover the target’s vulnerabilities. The next step is to exploit those vulnerabilities by gaining access to privileged information, stealing data, modifying system configurations, intercepting traffic, and more to estimate the amount of damage they can cause to the target system. Some of the test scenarios for simulating cyberattacks are listed below:
    • Cross-Site Scripting
    • Security Misconfigurations
    • SQL Injection
    • Password Cracking
    • Caching Servers Attacks
    • Cross-Site Request Forgery
    • File Upload flaws
    • Broken authentication and session management
  • Analysis and reporting

    A detailed report is compiled to outline the significant findings of the test process. The report includes all the details such as sensitive data exposed, a list of exploited vulnerabilities, the time duration for which the tester could maintain undetected access to the system, etc. This information is shared with the security personnel to analyze and configure the company’s WAF settings, fix the most critical parts, and implement application security policies to patch vulnerabilities and protect against future threats. 


Web Application Penetration Testing

Web Application Penetration Testing: Conclusion

Web applications are the primary source of business for numerous companies. With thousands of transactions taking place every second, securing these applications from attacks and data theft becomes crucial. Web application penetration testing can help organizations achieve the highest system security and prepare for any potential threat. Security personnel can leverage the latest testing tools to examine the existing source code, servers, WAF, database connectivity, APIs, third-party integrations, etc., to discover vulnerabilities, mitigate risks, and update security policies.

Excellent security measures are intrinsic to a great web application, but so are superior software developers. So if you’re looking to scale your software development team, try Turing. 

Turing’s automated platform lets companies “push a button” to hire senior, pre-vetted remote software developers. Access a talent pool of the top 1% of 1M+ developers with strong technical and communication skills who work in your time zone. There’s no risk. Turing offers a free two-week trial period to make sure your developers deliver to your standards.

For more information, visit Turing’s Hire page.

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By Oct 14, 2021