The release of ChatGPT 3.5 has changed everything for us programmers. Even though most of us (including me) don’t understand how it works, some of us use it more frequently than Stack Overflow, Google, and IDE built-in features. I believe this is just the beginning. Even though, only Microsoft knows what will happen next, let me try to make a humble prediction too. Below, I list what I believe robots (with Generative AI on board) will do in the future. The further into the future, the lower on the list. I tried not to repeat what GitHubNext is already saying.
Report Bugs. They will go through the codebase, analyze the code, and maybe even try to run some tests, then submit bug reports when problems are obvious. They will also submit bug reports when they find code that is hard to understand, improperly documented, not covered by automated tests, or has security vulnerabilities. Additionally, they will report when they see that the code is not following conventions or best practices. They will write their reports so nicely and provide so many technical details and supplementary links that programmers will prefer the reports from robots much more than reports from humans.
Review Pull Requests. They will examine the pull requests submitted to the repository (either by humans or robots) and review them by making comments on certain lines of code, either criticizing the quality of the code and/or suggesting better alternatives. They will keep track of the suggestions made earlier and will insist where necessary. In the end, the authors of the pull requests won’t even know who is reviewing them: a human or a robot.
Refactor. From a huge collection of well-known micro-refactorings, they will select the few most important at any given moment and will submit pull requests with the changes. They won’t alter the functionality of the code or make massive modifications. Instead, they will improve the quality of the code in small increments, making it easy for us humans to merge their suggested changes. They won’t change too much, so we won’t feel managed by robots, but we will be. Slowly and incrementally, they will improve the codebase, making it more readable, maintainable, and better understood … by other robots.
Backlog Prioritization. They will sort tasks and tickets into their appropriate milestones, determining which ones are of higher priority. They will decide which bug should be fixed first and which feature request is more important than others. Utilizing historical data, current team velocity, and other relevant metrics, they will create a prioritized backlog that aligns with both short-term objectives and long-term goals.
Refine Bug Reports. They will examine already reported bugs and refine them, providing supplementary information, explaining the code to which the bug refers, and suggesting code snippets that could potentially reproduce the bug. They will do the work that most programmers are too lazy to do: properly explain the bug in order to help its fixer.
Document Source Code. They will find places in the code that are hard to comprehend, such as complex functions, large classes, and big data structures. They will generate documentation blocks and then submit pull requests with them. Humans will be happy to accept these, since documenting someone else’s code is a routine and boring part of work. Moreover, keeping the documentation in sync with the source code is one of the areas where our human negligence is most visible.
Fix Bugs. According to the code they already see in the codebase and the list of bugs reported in issues, they will generate some fixes and submit them as new pull requests. They will explain what the fixes are doing, why the improvement is made in this or that way, how critical the fix is, and also suggest possible alternatives. We will simply merge them.
Formalize Requirements. They will examine the codebase and the comments where we discuss it, and will derive a formal definition of the requirements we implement. Then, they will formulate the requirements using Use Case diagrams, Requirement Matrix, or even informal textual documents like README or Wiki. They will keep these documents up to date throughout the entire lifecycle of the codebase—something we humans are often too lazy to do.
Onboard: They will assist in the onboarding process of new developers, guiding them through the codebase, explaining architectural decisions, and offering personalized tutorials. They will also help us understand certain code blocks by providing interactive guidance.
Analyze Technical Debt They could analyze the codebase to identify areas where technical debt is accumulating and suggest steps to mitigate it before it becomes problematic. They will submit tickets where the biggest debt territories will be identified and improvements suggested.
Cleanup Documentation. They will reformat the doc blocks that we humans write for our classes and methods, and then submit pull requests with the changes. Formatting the documentation correctly, using HTML, Markdown, Doxia, and many other formats, is a boring work where we humans fall short.
Suggest New Features. They will examine already implemented functionality and will suggest additional features, submitting tickets. They will explain the reasons behind such new feature requests, find proper justification, and provide examples of how users will interact with the new functionality.
Document Architecture. They will observe the codebase and then update the documentation about the architecture it implements. This is something programmers usually forget to do, or simply don’t know how to do right. The robots will use UML or perhaps less formal instruments to document the architecture, thus making the entire product easier to maintain.
Estimate. They will estimate the complexity of every bug report or feature request in staff-hours, calendar days, or maybe even in lines of code. This information will help the team make planning decisions.
Predict. By examining events in a repository, they will spot anomalies in our human behavior, such as changes in the mood of programmers in the comments, spikes in the intensity of commits, failures in CI/CD pipelines, and so on. They will be able to predict larger troubles before it’s too late. They will predict and then suggest corrective and preventive actions, submitting tickets with management or technical suggestions.
Appraise. They will observe the activity of every programmer and will appraise their productivity. The results will be published directly to GitHub issues or perhaps sent to project managers by email. In the end, they will decide who of us humans are more valuable to their projects.
I’m thankful to ChatGPT for helping me build this list.
What do you think
I we missed?