How do open source software (OSS) developers practice and perceive requirements engineering? An empirical study
Bibliographic record
Abstract
In open source software (OSS) development domain (a largely volunteer driven, geographically distributed, web based form of software development), it is mainly the OSS developers who are responsible for overseeing and managing the develop-mental activities. Existing OSS literature, based on qualitative analysis of web-based artifacts (e.g. data on discussion forums, issue databases) of a few OSS projects, report that requirements generation in OSS development is largely informal and ad hoc. But there is lack of an empirical study involving the practitioners themselves i.e. the OSS developers. We conducted a web-based survey among OSS developers in order to gain insights in to how they actually practice requirements engineering activities and what are their perceptions about it. For 57 requirements engineering practices obtained from closed source software development (CSSD) literature, the respondents indicated whether they currently used those practices in their OSS projects and whether those practices were useful for OSS development. The analysis of survey responses revealed that OSS developers used requirements engineering practices (from CSSD) significantly less in their developmental activities than what they believed they should have, indicated through usefulness ratings. We also asked participating OSS developers to indicate their perceptions about the usage of five informal requirements generation activities re-ported in OSS literature (e.g. developers simply asserting the requirements instead of eliciting). Subsequent analysis revealed that OSS developers used informal requirements generation activities significantly more than requirements elicitation practices (from CSSD) in their developmental activities. We use the survey findings to discuss implications for practice and research.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".