Why Do Software Developers Use Static Analysis Tools? A User-Centered Study of Developer Needs and Motivations
Bibliographic record
Abstract
As increasingly complex software is developed every day, a growing number of companies use static analysis tools to reason about program properties ranging from simple coding style rules to more advanced software bugs, to multi-tier security vulnerabilities. While increasingly complex analyses are created, developer support must also be updated to ensure that the tools are used to their best potential. Past research in the usability of static analysis tools has primarily focused on usability issues encountered by software developers, and the causes of those issues in analysis tools. In this article, we adopt a more user-centered approach, and aim at understanding why software developers use analysis tools, which decisions they make when using those tools, what they look for when making those decisions, and the motivation behind their strategies. This approach allows us to derive new tool requirements that closely support software developers (e.g., systems for recommending warnings to fix that take developer knowledge into account), and also open novel avenues for further static-analysis research such as collaborative user interfaces for analysis warnings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".