Introduction to the special issue on software engineering in practice
Bibliographic record
Abstract
The ever increasing complexity of software and rapidly changing development environments continues to drive the evolution of new technologies, techniques, and tools. This special issue, Software Engineering in Practice, provides the software engineering community with a valuable collection of current high-quality research articles that explore topics driven by real problems in industry. The inspiration for this special issue has drawn upon the ICSE Software Engineering in Practice Track (ICSE SEIP 2019)1, part of the Industry Program at the 41st International Conference on Software Engineering2; it builds upon the success of the previous special issue with the same theme.1 The ICSE SEIP Track provides a premier venue for researchers and practitioners to discuss innovations and solutions to concrete software engineering problems. The guest coeditor team for this special issue is an international collaboration involving the co-organizers of the ICSE SEIP 2019 Track, Helen Sharp and Michael Whalen, and two editors of the Journal of Software: Practice and Experience3 (JSPE), Judith Bishop and Kendra M. L. Cooper. The Call for Papers was designed to encourage submissions that presented novel and innovative ideas that broadly spanned the software engineering discipline; ideas that provided rigorously validated solutions for real problems encountered by practitioners. In order to promote an inclusive environment, the call was broadly disseminated as an open call; it was advertised on the JSPE and the ICSE SEIP 2019 websites, established software engineering newsgroups (eg, SEWORLD) and conference announcement sites (eg, WikiCFP), in addition to numerous professional and social media platforms. The call required submissions be original manuscripts that had not been previously published and were also not under consideration for publication elsewhere. Submissions of research article, survey papers, short communication, and extended conference papers were welcome; extended conference papers were required to include at least 30% additional novel contributions. The response from the software engineering community was enthusiastic: the special issue received 25 manuscripts submitted by authors from 13 countries. Submissions featured international collaborations from researchers in academia and industry, cases studies from industry, and the use of open source data sets and systems provided by the broader community. The submissions were reviewed according to the JSPE standards, with a goal of publishing the online version of the articles in a timely fashion. Ultimately, six articles were selected for the special issue. Overviews of these accepted manuscripts are presented below, organized into two groups. Novel contributions in the area of intelligent code analysis are explored in several of the papers in the special issue. Kim et al present an automated code analysis approach based on machine learning to recommend an appropriate level for logging runtime events. Rong et al present an automated code analysis approach based on templates and rules to generate documentation in a timely manner within agile DevOps environments. In addition, Huang et al present an automated code analysis approach to identify the need for header comments with the goal of supporting the long-term evolution of the product. Several of the papers in the special issue are related to the broader topic of reuse, from different perspectives. Weir et al present an on-going lightweight security training program that has the potential to be reused by a wide range of development teams; the research has a grounded theory foundation. Koziolek et al present a reference architecture to further the standardization and automation of IoT integration. Hu et al present a data mining-based approach to search and filter existing code samples with the goal of establishing high quality software repositories. We would like to extend our warmest thanks to all the authors who submitted their manuscripts, the anonymous reviewers who provided timely, high-quality review comments in their generous service to the community, and the JSPE editorial board and personnel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.150 | 0.085 |
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".