Improving IDE recommendations by considering global implications of existing recommendations

Muslu,; Brun, Yuriy; Holmes,; Ernst, F.; Notkin,

doi:10.1109/icse.2012.6227082

Cited by 4 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our personalized idea could be applied to recommendation systems [53], [54], [55] and other types of predictions, such as top crashes [56], quality attributes [57], bug-fixing commits [58], vulnerabilities [59], bug location and number of bugs [37]. Since each developer may have different software development behaviors, personalized systems can effectively learn from a developer and provide useful information for the developer.…”

Section: Discussionmentioning

confidence: 99%

Personalized defect prediction

Jiang

Tan

Kim

2013

2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE)

211

164

View full text Add to dashboard Cite

Abstract-Many defect prediction techniques have been proposed. While they often take the author of the code into consideration, none of these techniques build a separate prediction model for each developer. Different developers have different coding styles, commit frequencies, and experience levels, causing different defect patterns. When the defects of different developers are combined, such differences are obscured, hurting prediction performance.This paper proposes personalized defect prediction-building a separate prediction model for each developer to predict software defects. As a proof of concept, we apply our personalized defect prediction to classify defects at the file change level. We evaluate our personalized change classification technique on six large software projects written in C and Java-the Linux kernel, PostgreSQL, Xorg, Eclipse, Lucene and Jackrabbit. Our personalized approach can discover up to 155 more bugs than the traditional change classification (210 versus 55) if developers inspect the top 20% lines of code that are predicted buggy. In addition, our approach improves the F1-score by 0.01-0.06 compared to the traditional change classification.

show abstract

Section: Discussionmentioning

confidence: 99%

Personalized defect prediction

Jiang

Tan

Kim

2013

2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE)

211

164

View full text Add to dashboard Cite

show abstract

“…In fact, whilst refactoring emerged to facilitate the improvement of software design in existing software systems, code quick-fix and recommendation tools (e.g. jDeodorant) included in popular IDEs (Integrated Development Environments) aid software developers in automatically solving compiler errors or software design and implementation defects [40][41][42][43][44]. Given the similarity between code error and ontology inconsistency, quick-fixes could be successfully applied to knowledge engineering to automatically fix inconsistencies detected in ontologies by running recommended automated actions.…”

Section: Evolution From Software Engineering Quality Metrics To Ontology Quality Indicatorsmentioning

confidence: 99%

An ontology knowledge inspection methodology for quality assessment and continuous improvement

Roldan-Molina

Ruano-Ordás

Basto‐Fernandes

et al. 2021

Data & Knowledge Engineering

View full text Add to dashboard Cite

“…Many editors provide contextually relevant suggestions. Indeed, suggestion generation is closely related to several major research areas: code completion [32,18], program synthesis [17], and program repair [24,31,25,22].…”

Section: :8mentioning

confidence: 99%

Toward Semantic Foundations for Program Editors

Omar,

Voysey,

Hilton

et al. 2017

Preprint

View full text Add to dashboard Cite

Programming language definitions assign formal meaning to complete programs. Programmers, however, spend a substantial amount of time interacting with incomplete programs -programs with holes, type inconsistencies and binding inconsistencies -using tools like program editors and live programming environments (which interleave editing and evaluation). Semanticists have done comparatively little to formally characterize (1) the static and dynamic semantics of incomplete programs; (2) the actions available to programmers as they edit and inspect incomplete programs; and (3) the behavior of editor services that suggest likely edit actions to the programmer.This paper serves as a vision statement for a research program that seeks to develop these "missing" semantic foundations. Our hope is that these contributions, which will take the form of a series of simple formal calculi equipped with a tractable metatheory, will guide the design of a variety of current and future interactive programming tools, much as various lambda calculi have guided modern language designs. Our own research will apply these principles in the design of Hazel, an experimental live lab notebook programming environment designed for data science tasks. We plan to co-design the Hazel language with the editor so that we can explore concepts such as edit-time semantic conflict resolution mechanisms and mechanisms that allow library providers to install library-specific editor services.

show abstract

Improving IDE recommendations by considering global implications of existing recommendations

Cited by 4 publications

References 8 publications

Personalized defect prediction

Personalized defect prediction

An ontology knowledge inspection methodology for quality assessment and continuous improvement

Toward Semantic Foundations for Program Editors

Contact Info

Product

Resources

About