Preface to the special issue on program comprehension
Bibliographic record
Abstract
We are excited to present six selected papers of the 26th IEEE/ACM International Conference on Program Comprehension 2018, which took place in Gothenburg, Sweden, together with the 40th International Conference on Software Engineering.We received 69 submissions in total, of which we could accept 26.Each paper received at least three reviews and was discussed online, following a triple blind model, such that the reviewers did not know the identities of the authors, and that reviewers even did not know the identities of the other reviewers.The PC chairs selected papers to be invited for the special issue, such that all nominees for a distinguished paper award were invited.Furthermore, papers with a positive average score (1.0 on a 4 point scale from -2 to 2) and discussions among the PC members were also considered, as well as suggestions from the PC members.This resulted in the invitation of six papers, which all could be accepted for publication after considerable extension according to EMSE standard.This first invited paper, which received a distinguished paper award, evaluated the cognitive load of developers.The author team of Sarah Fakhoury, Devjeet Roy, Yuzhan Ma, Venera Arnaoudova, and Olusola Adesope contribute an extended version entitled "Measuring the Impact of Lexical and Structural Inconsistencies on Developers' Cognitive Load during Bug Localization".The paper presents a multi-modal approach to assess developers cognitive load based on a combination of functional near-infrared spectroscopy (fNIRS) and eye tracking.In addition to demonstrating the reliability of their multimodal approach by comparing the selfestimated cognitive load of participants with sensor information, the authors found evidence that changing the structure of code (e.g., violating coding conventions) does not increase cognitive load, but violating naming conventions for identifiers does.Additionally, the modalities to assess cognitive load all seem to capture different aspects of task difficulty.The second invited paper also received a distinguished paper award.The author team of Xing Hu, Ge Li, Xin Xia, David Lo, and Zhi Jin contribute their extended version entitled "Deep Code Comment Generation with Hybrid Lexical and Syntactical Information".In their paper, the authors develop an approach to automatically generate comments to Java methods,
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.186 | 0.109 |
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".