Introduction to the special issue on software engineering in practice
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,006 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,014 | 0,009 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,006 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,150 | 0,085 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».