Case Studies for Enhancing Student Engagement and Active Learning in Software V&V Education
Bibliographic record
Abstract
Two critical problems facing the software (S/W) industry today are the lack of appreciation of the full benefits that can be derived from Software Verification and Validation (V&V) and an associated problem of shortage of adequately trained V&V practitioners. To address this situation, the software V&V course curriculum at the author’s institution is being improved via a National Science Foundation (NSF)-funded project. The basic objectives of this project are to enhance the quality of software education via increased student engagement and by bridging the gap between the basic principles discussed in the classroom and the complexity of real world problems. The teaching method utilized promotes higher levels of student engagement and learning through interactive, hands-on exercises, case studies and discussions. In addition, the instructional materials were purposefully designed not only for university classroom settings, but to also be deployed for on-the-job professional training in S/W industry settings, thereby helping to increase the pool of professionals with contemporary V&V knowledge and skills. The new course curriculum enhancement described in this paper is guided by academic research and industry best practices that focus on four specific V&V focus areas: requirements engineering, reviews, configuration management, and testing. Among many educational tools that are being developed to achieve the project objectives, the work related specifically to the development of one central component, case studies, is described here. Historically, case studies have been educational tools utilized in business, law, and medicine, but are not as prominent in software engineering. The hypothesis is that case studies would be effective educational tools to introduce real-world professional practices into the classroom, which would help the students in both identifying and solving problems, and developing a perspective on applying knowledge. In this paper we describe a set of V&V related case-studies that we have drawn from industry experiences and developed as pedagogical tools. These case-studies cover several important topics in the S/W V&V domain such as software testing, legal issues in software, software consumer protection, and requirements from the customers’ perspectives. We also report on the results of initial implementation of the case studies related to software testing in the classroom and show their effectiveness both in terms of satisfying Accreditation Board for Engineering and Technology (ABET) outcomes assessment and student satisfaction.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".