A Service Sharing Approach to Integrating Program Comprehension Tools
Bibliographic record
Abstract
Software maintenance is the most time consuming and costly phase of the software development lifecycle. For every dollar spent on creating a new software system, nine dollars is spent on maintaining it throughout its useful life. By the late 1980s maintenance spending accounted for an estimated US$30 billion worldwide. Any activity that even minimally reduces maintenance efforts would yield significant cost savings within the software industry [3]. Tool support for maintainers has focused largely on providing assistance in activities related to program comprehension. The goal of these tools is to provide a rapid means for maintainers to understand large scale software systems. Most program comprehension tools have a specific strength or specialized application area [10] but are weak in other areas. No single tool exists that provides all the functionality and flexibility that most software maintainers need. For this reason, research attention has been focused on getting program comprehension tools to integrate with each other. In this paper we present a novel approach to facilitating integration among tools used by maintainers to assist in program comprehension. We start by showing that program comprehension tools have many similar characteristics. Taking full advantage of this fact, we outline how specially designed adapters and a domain ontology can be used together to allow these tools to integrate transparently with each other.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".