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Enregistrement W2544185825 · doi:10.1002/smr.1836

Guest editor's introduction to the Special Issue on Program Comprehension (ICPC 2014)

2016· article· en· W2544185825 sur OpenAlexaff
Chanchal K. Roy, Andrew Begel, Leon Moonen

Notice bibliographique

RevueJournal of Software Evolution and Process · 2016
Typearticle
Langueen
DomaineComputer Science
ThématiqueSoftware Engineering Research
Établissements canadiensUniversity of Saskatchewan
Organismes subventionnairesnon disponible
Mots-clésProgram comprehensionComputer scienceComprehensionSoftware engineeringBusiness process reengineeringSoftwareInclusion (mineral)World Wide WebSoftware systemEngineeringOperations managementPsychologyProgramming language

Résumé

récupéré en direct d'OpenAlex

It is our pleasure to introduce you to the papers in this Special Issue based on the 22th International Conference on Program Comprehension (ICPC 2014). Program comprehension plays a central role in most of the phases of the software development life cycle, where it helps facilitate reuse, inspection, maintenance, reverse engineering, reengineering, migration, and extension of existing software systems. The International Conference on Program Comprehension (ICPC) is the primary venue for work in the area of program comprehension. It is also one of the leading venues for work in the areas of software analysis, reverse engineering, software evolution, and software visualization. ICPC provides an opportunity for researchers and industry practitioners to present and discuss the state-of-the-art and the state-of-the-practice in program comprehension and related areas. ICPC 2014 took place during June 2–3, 2014, in Hyderabad, India, and was co-located with the International Conference on Software Engineering (ICSE 2014). ICPC 2014 received a record number of submissions (76) from 19 different countries, which allowed us to assemble an excellent program that continues ICPC's tradition of providing a high-quality venue for sharing the latest advances in program comprehension. The program included 20 full research papers, 11 short papers and 5 tool demonstration papers. Of these 20 full research papers, five were invited to submit an extended version to the Journal of Software: Evolution and Process. After a rigorous reviewing process with at least three reviewers per paper, four papers were accepted for publication in this special section. The paper entitled ‘Framing Program Comprehension as Fault Localization’ by Alexandre Perez and Rui Abreu proposes an approach, coined Spectrum-based Feature Comprehension (SFC), that borrows techniques from software-fault localization that were proven to be effective even when debugging large applications. SFC analyses the program by exploiting run-time information from test case executions to identify the components that are important for a given feature, helping software engineers to understand how a program is structured and each of the functionality's dependencies are. They present a toolset, coined PANGOLIN, that implements SFC and displays its report to the user using an intuitive visualization. The paper entitled ‘Searching Crowd Knowledge to Recommend Solutions for API Usage Tasks’ by Eduardo C. Campos, Lucas B. L. de Souza, and Marcelo de A. Maia presents an approach that makes use of ‘crowd knowledge’ in Stack Overflow to recommend information that can assist developer activities. This strategy recommends a ranked list of question-answer pairs from Stack Overflow based on a query. The criteria for ranking are based on three main aspects: the textual similarity of the pairs with respect to the query related to the developer's problem, the quality of the pairs, and a filtering mechanism that considers only ‘how-to’ posts. The paper entitled, ‘AmaLgam+: Composing Rich Information Sources for Accurate Bug Localization’ by Shaowei Wang and David Lo proposes AmaLgam+, which is a method for locating relevant buggy files that combines five sources of information, namely, version history, similar reports, structure, stack traces, and reporter information. They perform a large-scale experiment on four open source projects, namely, AspectJ, Eclipse, SWT, and ZXing to localize more than 3000 bugs and compare AmaLgam+ results with those of six state-of-the-art bug localization approaches. The study showed that the newly proposed method outperforms these existing approaches in terms of mean average precision. The paper entitled, ‘Source code analysis with LDA’ by David Binkley, Daniel Heinz, Dawn Lawrie and Justin Overfelt aims to aid software engineers in their understanding of the Latent Dirichlet allocation (LDA) tuning parameters by numerically and graphically demonstrating the relationship between the tuning parameters and the LDA output. LDA has seen increasing use in the understanding of source code and its related artifacts in part because of its impressive modeling power. However, this expressive power comes at a cost: The technique includes several tuning parameters whose impact on the resulting LDA model must be carefully considered. The aim of their work is to provide insights into the tuning parameters' impact. We hope that readers will enjoy this special issue and gain useful insights from the four papers presented. We would like to thank all the authors who submitted papers to the conference and the authors who significantly extending their work for this special issue. In addition, we would like to thank the members of the ICPC 2014 program committee and the external reviewers for their time, careful reviews, and active discussions of the submitted papers, which helped making this special issue special. Finally, we would like to thank the editorial board of the Journal of Software: Evolution and Process and the publisher Wiley for providing us with the opportunity to devote this issue to the best of ICPC 2014. We also thank Journal of Software: Evolution and Process Editor Gerardo Canfora for providing expert guidance and important advice throughout the process. Enjoy!

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,032
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,072
Score d'incertitude au seuil0,240

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,032
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0050,003
Études des sciences et des technologies0,0020,001
Communication savante0,0100,006
Science ouverte0,0030,003
Intégrité de la recherche0,0040,007
Charge utile insuffisante (le modèle a refusé de juger)0,0720,035

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.

Tête enseignante Opus0,009
Tête enseignante GPT0,273
Écart entre enseignants0,264 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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