Using Academia-Industry Partnerships to Enhance Software Verification & Validation Education via Active Learning Tools
Bibliographic record
Abstract
Imparting real world experiences in a software verification and validation (SV&V) course is often a challenge due to the lack of effective active learning tools. This pedagogical requirement is important because graduates are expected to develop software that meets rigorous quality standards in functional and application domains. Realizing the necessity the authors designed and developed 42 delivery hours of active learning tools consisting of Case Studies, Class Exercises, and Case Study Videos for use in courses that impart knowledge on SV&V topics viz. requirements engineering, software reviews, configuration management, and software testing. Four key skill areas sought after by employers, namely communication skills, applied knowledge of methods, applied knowledge of tools, and research exposure are used to drive the development funded by a National Science Foundation grant and perfected through an industry-academia partnership.In this paper, we discuss in detail the four project plans the researchers and their industry counterparts followed over the past two years in the development and eventual dissemination of the active learning tools. A course enhancement plan was used to drive activities related to reviewing, enhancing, and modularizing modules, identified by a gap analysis performed by focus groups comprised of industry and academic partners. The course delivery plan was used to drive activities related to developing content delivery strategies. An evaluation and assessment plan was used to drive activities related to periodically evaluating student learning and assessing the project. And finally a course dissemination plan is being used to drive activities related to distributing course modules and assessment reports. The tools have been shared through two workshops and other means with instructors in universities and industry partners.
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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 | 0.050 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".