Studying developer build issues and debugger usage via timeline analysis in visual studio IDE
Bibliographic record
Abstract
Every day, most software developers use development tools to write, build, and maintain their code. The most crucial of such tools is the integrated development environment (IDE), in which developers create and build code. Therefore, it is important to understand how developers perform their work and what impact each action has on their workflow to further enhance their productivity. In this work, we study the KaVE dataset of developer interactions within the Microsoft Visual Studio IDE and analyze a number of topics extracted from the data. First, we propose a method for developing what we call "timelines" that chronologically map an individual development session, and from this, we study build failures, code debugger usage, and we propose a metric for measuring developer throughput. We find that the timeline analysis may prove to be an invaluable tool for developer self-assessment and key to uncovering problem areas regarding build failures. Moreover, we find that developers spend a significant amount of time debugging their code, utilizing features such as breakpoints to resolve issues. Finally, we see that the developer metric can be used for self assessment, giving value to the amount of effort, put forth by a developer, in a given session.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".