Keynote 2 : From Data to Development: Empirical Approaches in Software Engineering
Bibliographic record
Abstract
Empirical research plays a critical role in advancing software engineering by collecting and analyzing both quantitative and qualitative data to enhance software products, development processes, and project management. Core empirical methods, which includes controlled experiments, case studies, and surveys, offer distinct ways to investigate real-world challenges. These studies typically follow a structured process involving the design of the research setting, data collection, and data analysis. This keynote will provide an overview of key empirical research methods used in software engineering, highlighting their respective strengths and limitations. In particular, the talk introduces a novel method, dialog-based protocol analysis, as an extension to traditional approaches such as introspective, retrospective, and think-aloud techniques. A case study is presented to evaluate the effectiveness of this new method. The presentation concludes with a discussion on the current challenges and future opportunities for empirical research in the evolving landscape of software engineering.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".