Investigating quality factors in object-oriented designs: an industrial case study
Bibliographic record
Abstract
Article Free Access Share on Investigating quality factors in object-oriented designs: an industrial case study Authors: Lionel C. Briand Fraunhofer Institute for Experimental Software Engineering, Sauerwiesen 6, 67661 Kaiserslautern, Germany Fraunhofer Institute for Experimental Software Engineering, Sauerwiesen 6, 67661 Kaiserslautern, GermanyView Profile , Jürgen Wüst Fraunhofer Institute for Experimental Software Engineering, Sauerwiesen 6, 67661 Kaiserslautern, Germany Fraunhofer Institute for Experimental Software Engineering, Sauerwiesen 6, 67661 Kaiserslautern, GermanyView Profile , Stefan V. Ikonomovski Centre de Recherche, Informatique de Montreal, Sherbrooke West, Suite 100, Montréal, Qc, Canada H3A 1B9 Centre de Recherche, Informatique de Montreal, Sherbrooke West, Suite 100, Montréal, Qc, Canada H3A 1B9View Profile , Hakim Lounis Centre de Recherche, Informatique de Montreal, Sherbrooke West, Suite 100, Montréal, Qc, Canada H3A 1B9 Centre de Recherche, Informatique de Montreal, Sherbrooke West, Suite 100, Montréal, Qc, Canada H3A 1B9View Profile Authors Info & Claims ICSE '99: Proceedings of the 21st international conference on Software engineeringMay 1999 Pages 345–354https://doi.org/10.1145/302405.302654Online:16 May 1999Publication History 145citation1,486DownloadsMetricsTotal Citations145Total Downloads1,486Last 12 Months39Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".