Bibliographic record
Abstract
The steady rise of open source software (OSS) (Raymond, 1999) over the last few decades has made a noticeable impact on many sectors of society where software has a role to play. As reflected from the frequency of media articles, traffic on mailing lists, and growing research literature, OSS has garnered much support in the software community. Indeed, from the early days of GNU software, to X Window System, to Linux and its utilities, and more recently the Apache Software Project, OSS has changed the way software is developed and used. As the deployment of OSS increases, the issue of its quality with respect to its stakeholders arises. We contend that the open source community collectively bears responsibility of producing “high-quality” OSS. Lack of quality raises various risks for organizations adopting OSS (Golden, 2004). This article discusses the manifestation of quality in open source software development (OSSD) from a traditional software engineering standpoint. The organization is as follows. We first outline the background and related work necessary for the discussion that follows, and state our position. This is followed by a detailed treatment of key software engineering practices that directly or indirectly impact the quality of OSS. Next, challenges and directions for future research are outlined and, finally, concluding remarks are given.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".