Intelligent Analysis of Software Maintenance Data
Bibliographic record
Abstract
Amount of software engineering data gathered by software companies amplifies importance of tools and techniques dedicated to processing and analysis of data. More and more methods are being developed to extract knowledge from data and build data models. In such cases, selection of the most suitable data processing methods and quality of extracted knowledge is of great importance. Software maintenance is one of the most time and effort-consuming tasks among all phases of a software life cycle. Maintenance managers and personnel look for methods and tools supporting analysis of software maintenance data in order to gain knowledge needed to prepare better plans and schedules of software maintenance activities. Software engineering data models should provide quantitative as well as qualitative outputs. It is desirable to build these models based on a well-delineated logic structure. Such models would enhance maintainers’ understanding of factors which influence maintenance efforts. This chapter focuses on defect-related activities that are the core of corrective maintenance. Two aspects of these activities are considered: a number of software components that have to be examined during a defect removing process, and time needed to remove a single defect. Analysis of the available datasets leads to development of data models, extraction of IF-THEN rules from these models, and construction of ensemble-based prediction systems that are built based on these data models. The data models are developed using well-known tools such as See5/C5.0 and 4cRuleBuilder, and a new multi-level evolutionary-based algorithm. Single data models are put together into ensemble prediction systems that use elements of evidence theory for the purpose of inference about a degree of belief in the final prediction.Request access from your librarian to read this chapter's full text.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".