JetBrains

Early evidence suggests that, if not used judiciously, AI-powered coding assistants can erode the quality of codebases. Why does this happen, and how can it be prevented? AI coding assistants often focus on adding new code, while developers must also bug-fix and refactor existing code. Another problem is that most coding assistants are incentivized to make suggestions that will be accepted and not generate code that is easy to maintain. However, improving the quality of suggestions alone is unlikely to solve the problem. The real challenge is how to integrate AI-generated and human-written code in a way that preserves or improves the health of codebases. One way to address an AI-driven code quality gap could be to combine traditional code quality best practices like testing, refactoring, static analysis, and code reviews with novel AI-driven solutions. For example, when the JetBrains research team used static analysis to enhance and rank refactoring suggestions generated by LLMs, 81% of surveyed developers found the suggestions useful. Read the full article to explore the problem of the AI-driven code quality gap: https://lnkd.in/defkN_QY

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How can you empower teams to maintain high-quality code? Here are the top issues that leaders need to address. ⏳ Time constraints are the number one reason teams fail to address code quality issues, with 48% of respondents in the Developer Ecosystem Survey conducted by JetBrains selecting them as the biggest obstacle to maintaining high-quality code. As a leader, you can implement better time management techniques or invest in automation and tools to reduce repetitive tasks, freeing time for quality-focused work. 👷 The second-most common reason is high workload, with 38% of responses. Workload can be balanced by hiring additional talent or improving task prioritization. Additionally, leveraging AI to automate low-value tasks can free up your team’s time to focus on more complex and valuable work. 🗒️ Misaligned code quality guidelines come in third, with 34% of responses. Ensure clear, team-wide understanding of code quality standards by holding regular discussions and aligning them with business goals. How are you ensuring your team stays focused on code quality despite these challenges? Let us know in the comments!

Built on more than 20 years of expertise in static code analysis, JetBrains AI offers developers several unique advantages through its deep integration with JetBrains IDEs. The integration leverages advanced static analysis, contextual understanding, and powerful refactoring capabilities, creating a more cohesive and intuitive coding experience. Thanks to JetBrains AI, developers receive accurate code suggestions within their familiar development environments, boosting productivity. Organizational benefits include higher team efficiency and improved code quality through the seamless integration of AI-generated and human-written code. To learn more, watch the full video with the JetBrains AI Director Vladislav Tankov: https://lnkd.in/d5AQPb_Z

Shorter product delivery lead times provide faster feedback, enabling teams to adjust and improve quickly. Frequent deployments help teams stay agile and responsive to changes. We're curious to know how often your team pushes code to production. Please share your experience with us!

Year after year, testing consistently takes third place in the Developer Ecosystem report's list of activities after coding and code reviewing. This reflects the importance of testing in the software development process. As software becomes increasingly complex, more and more developers are getting involved in testing early in the development process. By integrating testing earlier, teams can catch defects sooner, reduce costs, and improve software quality. This method, known as the "shift-left approach", reduces time to market and ensures higher-quality releases. The Developer Ecosystem data indicates that 51% of respondents are involved in testing activities. While this is a significant portion, there's definitely room for improvement. A 2023 study states insufficient testing is the third most frequently reported hindrance to developers' productivity at Google. https://lnkd.in/eX62ESac Encouraging more developers to adopt testing practices early in the development process can enhance overall software quality. There are several tactics that can help achieve this aim: ensure rapid feedback loops so developers can see the impact of their tests quickly, allocate time for testing, and, more importantly, encourage collaboration between developers and testers. How do you encourage your developers to test their software?

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How do you get your iOS engineers on board with Kotlin Multiplatform? What concerns do they have? As Annyce Davis noted during KotlinConfersations'24, iOS developers are worried about their job security, reduced autonomy, and developer experience. All of these concerns are valid, and a leader can address them by maintaining continuous dialogue, listening to their feedback, and setting up pair programming sessions. This approach can eventually enable iOS developers to contribute confidently to a KMP codebase. Watch this video to dive into Annyce's journey in adopting Kotlin Multiplatform (KMP) and her experiences in engineering leadership: https://lnkd.in/eeaHyxvs

Early evidence shows that AI assistants usage can cause a decline in codebase health. What causes these problems? Here are three reasons why AI tools may degrade code quality: ◾The lure of the new: AI coding assistants often focus on adding new code, but developers must also bug-fix and refactor existing code. JetBrains researchers found that quality improvement – finding problems, refactoring code, and adjusting code to follow best practices – is one of the main usage patterns of JetBrains AI Assistant users. ◾Automation bias: Most coding assistants are incentivized to make suggestions that will be accepted and not to generate code that is easy to maintain. Developers risk placing too much trust in those suggestions or simply not putting enough effort into reviewing them before accepting. ◾Lack of a holistic view: Keeping the quality of AI-generated suggestions high is not enough to solve the quality problem. The challenge is how to integrate AI-generated and human-written code in a way that preserves or improves the health of codebases that may be in use for decades. Read the full article to explore the problem of the AI-driven code quality gap: https://lnkd.in/defkN_QY

A show of hands: Who has seen your team agree to finally document these internal libraries, only to return to the issue in several months with zero work done and no documentation to show? It’s a common scenario, isn’t it? Developers love good documentation because it helps them learn new libraries quickly and efficiently. The business value of good documentation is undeniable – it improves onboarding, makes maintenance easier, and reduces errors due to miscommunication. A 2023 study states poor documentation is the second most frequently reported hindrance to developers' productivity at Google. https://lnkd.in/eX62ESac However, creating documentation often takes a back seat amidst ever-growing task lists and tight deadlines. As a result, it’s frequently overlooked or rushed, leading to incomplete or outdated resources. So, how can leaders break this cycle? Here are a few strategies that might help: ▫️ Prioritize docs: Treat documentation as a critical part of the development process, not an afterthought. ▫️ Integrate documentation into your CI/CD pipeline: Have your team write documentation while coding to ensure it stays up to date. ▫️ Use the right tools: Leverage documentation tools that integrate seamlessly with your development environment. How does your team handle documentation? Share your tips and experiences in the comments below!