Software developers spend a significant portion of their resources handling user-submitted bug reports. For software that is widely deployed, the number of bug reports typically outstrips the resources available to triage them. As a result, some reports may be dealt with too slowly or not at all. We present a descriptive model of bug report quality based on a statistical analysis of surface features of over 27,000 publicly available bug reports for the Mozilla Firefox project. The model predicts whether a bug report is triaged within a given amount of time. Our analysis of this model has implications for bug reporting systems and suggests features that should be emphasized when composing bug reports. We evaluate our model empirically based on its hypothetical performance as an automatic filter of incoming bug reports. Our results show that our model performs significantly better than chance in terms of precision and recall. In addition, we show that our modelcan reduce the overall cost of software maintenance in a setting where the average cost of addressing a bug report is more than 2% of the cost of ignoring an important bug report.
From Automating Software Engineering to Empowering Software Developers
Machines today can write software, compose music, create art, predict events, and listen and learn from humans. Notably, automation also plays an essential role in high performing software development teams by automating tasks and improving developer productivity. But automation can’t (yet) replace human imagination and the intelligence that arises when multiple great minds work together to solve the complex problems that are inherent in software and systems design. In this talk, we will review how automation in modern software development has evolved and the many benefits it has brought. We will then explore how a deeper understanding of the developer experience points to untapped possibilities for innovating automation for software engineering, focusing on how they can:
support developers to manage the cognitive complexity of today’s systems,
ease and enhance collaboration by speeding up feedback loops, and
help developers to get in and stay in a state of flow when developing.
We will conclude by discussing how we can measure the impact of new innovations on the developer experience, and how doing so will drive actionable change and empower developers to do their best work joyfully.
