Improvement of Open Source Software Usability: An Empirical Evaluation from Developers' Perspective
Bibliographic record
Abstract
User satisfaction has always been important for software success whether it is Open Source Software (OSS) or closed proprietary software. Even though we do not presume that OSS always has poor usability, as there are examples of good usable open source software, it would still be agreed that OSS usability has room for further improvement. This paper presents an empirical investigation to study the impact of some key factors on OSS usability from developers' points of view. This is one of the series of four studies that we are conducting regarding improvement of OSS usability from OSS developers, users, contributors, and industry perspectives. The research model of this empirical investigation studies and establishes the relationship between the key usability factors from developers' perspective and OSS usability. A data set of 106 OSS developers from 18 open source projects of varied size has been used to study the research model. The results of this study provide empirical evidence that the studied key factors play a significant role in improving OSS usability.
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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 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".