Selecting the Right Topics for Industry-Academia Collaborations in Software Testing: An Experience Report
Bibliographic record
Abstract
The global software industry and the Software Engineering (SE) academia are two large communities. However, unfortunately, the level of joint industry-academia collaborations (IAC) in SE is still relatively very low, compared to the amount of activity in each of the two communities. Selecting the right topic for a new IAC has been reported to be challenging and often a deal-maker or-breaker for the start of IACs. Motivated by the above need, the goal of this paper is to propose experience-based guidelines from our 10+ software testing IACs in the past several years in Canada and Turkey to effectively and efficiently select right topics for IACs in software testing (also easily generalizable to other areas of SE), for the benefit of SE researchers and practitioners in starting new IACs. The experience and evidence supporting the guidelines in this paper are drawn from the authors' past projects and also seven on-going software-testing projects in the context of a large Turkish software and systems company. The topic-selection process has involved interaction with company representatives in the form of both multiple group discussions and separate face-to-face meetings while utilizing grounded-theory to find (converge to) topics which would be 'interesting' and useful from both industrial and academic perspectives. To increase the success of our topic selection process, we also utilized two other sources of information from the literature: (1) a set of four fitness criteria for topic selection in industry experiments, and (2) challenges and best practices for IAC, specific to project inception, as synthesized in a recent systematic literature review. We believe the results of this paper would be helpful for other researchers and practitioners not only in software testing but also in software engineering in general in increasing their chances of success in project inception and topic selection phase.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".