To become productive and proficient enough, developers new to software applications often require months, if not years, of on-the-job training to avoid making dangerous mistakes that put systems at risk. Discerning the true intent of the feature in the code is no easy feat, and developers today spend around 75% of their time researching the source code to identify the code representing the feature that needs to be changed or modernized. .
While code research tools, linters, and static and dynamic analysis tools can all help developers dramatically improve their efficiency and effectiveness, too many of these tools fall short when it comes to software development. ‘really identify specific lines of code that require special attention – particularly with how entangled in a system that code often is.
Current approaches used by today’s AI help improve this challenge, but they are insufficient in many ways. Fortunately, using a new approach to AI dramatically improves a developer’s ability to effectively identify specific lines of code relevant to the tasks they spend a tremendous amount of time on every day.
The shortcomings of today’s tools
As software repositories continue to grow unchecked to unprecedented sizes, those responsible for maintaining and preserving the functionality of a system say it is getting harder and harder to find bugs in code without machine help. Even worse, debugging consumes about half of a developer’s time, and fixing and then validating the fix for a single bug can take days, weeks, or even longer.
From locating bugs to repairing programs and summarizing code, today’s tools can be useful for analyzing millions of lines of code, reporting errors, and suggesting fixes or best practices, but a a number of challenges remain in existing code research tools. Navigating the code in a tool like Sourcegraph speeds up the speed at which developers can build a mental model of code, helping them understand the parts of the code universe that are most relevant and important, but the tool does not. not actually build the mental model. The tool only brings the developer closer to code that might be relevant to the intended behavior, but the developer has to reconstruct this mental model. Over time, with the mental challenge and mistakes that this effort can be, humans are ultimately fallible even though they use the best tools available to help them.
For example, security vulnerabilities are fairly straightforward to identify with today’s tools, but focusing on all the relevant code to fix is ââdifficult and time consuming, much longer than writing the program in the first place. Often the relevant code is scattered over many files, which increases the complexity of the task. When debugging unfamiliar programs, developers typically start the process with a âwhyâ question about their program’s behavior, but they must conceptualize this question into a series of actions and requests to resolve the issue at hand. As a result, programmers must simultaneously keep track of several aspects of the detailed program execution path, however, the ability to do so is incredibly difficult even for the most experienced developers.
If a programmer fails to understand how changing code in one area of ââthe code affects the system as a whole, even a minor adjustment can break the entire system. Since it is important for developers to understand what an application actually does, debuggers are not used as widely as you might expect, especially in functional programming environments.
Unfortunately, even the most advanced static and dynamic analysis tools, research tools, and linters are extremely ineffective in conveying this knowledge. Whether it’s drawing pretty pictures, plotting code, seeing how code behaves with specific input data, or providing a summary of searched words followed by complex changes, these tools help developers identify code relevant in different ways, but developers should always conceptualize to highlight the intent of previous developers coded in the software and reveal the bug they are looking to fix. Ultimately, even with the most advanced tools, human software developers still have to build a mental model of what the code does by directly deciphering the code to match the behavior they seek to change so that they can. effectively modify complex elements and critical systems.
Harnessing the power of AI to overcome the gaps
From Intel to Amazon to Microsoft, some of the world’s biggest tech companies are turning to and creating AI-powered tools that deliver snippets to developers as they code. By leveraging AI, developers see productivity gains, reduced time spent writing code, and reduced risk.
However, by using AI in a new way, the code repository can become a knowledge repository that represents source code the same way a human thinks of the world, in terms of cause and effect. When AI-based tools know each of the behaviors in the source code, the tools can then âcollaborateâ with developers to focus on the code that needs to change, without finding and understanding all the surrounding code. Using AI in this way can increase developer productivity by orders of magnitude.
By allowing developers to ask this repository of knowledge for the specific code of a behavior, AI helps developers process only relevant code, allowing developers to confidently make changes that they won’t break. the whole system. Best of all, developers can âproposeâ a change while they code without recompiling or checking in the code, so the use of AI mitigates risk by checking to see if the change is isolated. This capability is also extended to the CI / CD pipeline by having the AI ââverify the code after each build to ensure that the behavior never changes in the future.
Using AI in this way focuses a developer only on the code that matters, without the excessive cognitive load of understanding code that is irrelevant to the task at hand. Unlike code search tools, linters, and static and dynamic analysis tools, AI ultimately enables developers to safely, efficiently, and effectively make necessary changes..