ResearchICPC 2020

This track provides a quality forum for researchers and practitioners from academia, industry, and government to present and discuss new results, negative results, and replications in program comprehension.

Accepted Papers

Call for Papers

This track provides a quality forum for researchers and practitioners from academia, industry, and government to present and discuss new results, negative results, and replications in program comprehension. The International Conference on Program Comprehension (ICPC) welcomes submissions reporting:

1. New results because they advance our knowledge of program comprehension, which then serves as a basis to improve the practice and to understand further how developers’ understand software artifacts, processes, and evolution. "The measure of greatness in a scientific idea is the extent to which it stimulates thought and opens up new lines of research.” wrote Paul Dirac (1968).
2. Negative results because they allow us to constantly evaluate the hypotheses space and understand the state of the practice. As Walter Tichy (2000) writes, “Negative results, if trustworthy, are extremely important for narrowing down the search space. They eliminate useless hypotheses and thus reorient and speed up the search for better approaches.”
3. Replications because they solidify our knowledge, allow generalisations, and application in different situations. “If you’re going to start a new project… the first thing you should do is get up to the cutting edge of the field, and by far the best way to do that is to take the best article and to replicate it.” recalls Gary King (2011).

Format and Submission

Submissions must not be longer than 10 pages for the main text, inclusive of figures, tables, appendices. References only may be included on up to 2 additional pages. Purchases of additional pages in the proceedings are not allowed. The submissions must conform to the ACM Master Article Template, indifferent of the type-setting system (LaTeX, Word…). LaTeX users must use the provided class and bibliography style without modification:

\documentclass[sigconf,review]{acmart}
\bibliographystyle{ACM-Reference-Format}

The submissions must comply with the ACM Policy on Plagiarism, Misrepresentation, and Falsification. In particular, they must not have been published elsewhere and must not be under review elsewhere while under review for ICPC. The submission must also comply with the IEEE Policy on Authorship, part of the IEEE Plagiarism FAQ.

Submissions should not disclose the identity of the authors to comply with the double-blind review process employed by ICPC. The authors’ names must be omitted from the submissions and references to their prior work should be in the third person. Further advice, guidance, and explanation about the double-blind review process are available on the Q & A of ICSE 2020.

ICPC 2020 follows the ACM SIGSOFT rules on Conflicts of Interest and Confidentiality of Submissions and all authors, reviewers, and organizers will uphold the ACM Code of Ethics and Professional Conduct.

Categories and Evaluation Criteria

Submissions to ICPC 2020 Research Track will be reviewed by at least three members of the Program Committee. To facilitate the reviewing process of the submissions’ contributions, authors must select one of the following paper categories when submitting their papers and must add the name of the selected category at the beginning of the title of their papers in the submission site.

1. New Results

Required Contribution: Advance the knowledge or the practice of program comprehension by proposing new results about program comprehension, including theories, methods, and tools.

Evaluation Criteria: Submissions will be evaluated on the basis of their originality, importance to research and–or practice, technical soundness, evaluation, quality and consistency of presentation, and appropriate comparison to related work.

2. Negative Results

Required Contributions: Results contradicting developers’ lore, practical advice, or research hypotheses about program comprehension, including results disproving claims about some practices or results showing lack of expected effects of some novel techniques.

Evaluation Criteria: Submissions will be evaluated on the basis of importance and quality the methodology, depth of the discussions on the implications of the negative results, the amount of useful and actionable insights, quality and consistency of presentation, and appropriate comparison to related work.

3. Replications

Required Contributions: Replications of empirical studies related to program comprehension, including controlled experiments, case studies, and surveys. Submissions can report replications of the authors’ own works or replications of others’ works.

Evaluation Criteria: Submissions will be evaluated on the basis of the rigour of the methodology, choice of data set, and reporting; the depth of the discussions on the implications of the replication; the quality and consistency of presentation; and, appropriate comparison to related work.

Publication and Presentation

Upon notification of acceptance, all authors of accepted papers will receive further instructions for preparing the camera-ready versions of their submissions. At least one author of each submission must register and present the work at the conference; otherwise, the paper will be excluded from both the program and the proceedings. All accepted papers will be published in the conference electronic proceedings, which will also be available in the ACM Digital Library. The papers will be presented in a formal setting. Details about the presentations will follow the notifications.

Authors of selected papers from Research track will be invited to submit extended versions of their work to a special issue of Springer Journal of Empirical Software Engineering.

Program Co-chairs

Yann-Gaël Guéhéneuc and Shinpei Hayashi

ACM SIGSOFT Distinguished Paper Awards will be given to:

• An Empirical Study of Quick Remedy Commits; by Fengcai Wen, Csaba Nagy, Michele Lanza, and Gabriele Bavota (Software Institute, USI Università della Svizzera Italiana)
• A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning; by Fang Liu, Ge Li, Bolin Wei, Xin Xia, Zhiyi Fu, and Zhi Jin (Peking University; Monash University)