Ensemble

Xiang, Pei Feng; Ying, Annie T. T.; Cheng, Ping; Dang, Y. B.; Ehrlich, Kate; Helander, Mary E.; Matchen, Paul M.; Empere, A.; Tarr, Peri; Williams, Clay; Yang, Shun Xiang

doi:10.1145/1454247.1454259

Cited by 19 publications

(3 citation statements)

References 10 publications

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“…While a significant body of prior work has focused on making recommendations to support more general software engineering tasks such as finding experts to help with development tasks [37,39,40], keeping developers informed of stakeholders working on related tasks [52], or supporting the build process [49], there has been far less thought on how to utilize recommendation systems within the requirements discovery process. Felfernig et al [24] presented a visionary perspective of a "Recommendation and Decision Support System," which would support individual and group activities through recommending stakeholders for quality reviews, prioritizing requirements, suggesting relevant requirements for a current task, identifying dependencies among requirements, proposing changes that could be made to a requirements artifact to maximize group agreement, and identifying sets of requirements for a future release.…”

Section: Recommendation Systems In Requirements Engineeringmentioning

confidence: 99%

Recommendation Systems in Requirements Discovery

Hariri

Castro-Herrera

Cleland‐Huang

et al. 2013

Recommendation Systems in Software Engineering

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Recommendation systems offer the opportunity for supporting and enhancing a wide variety of activities in requirements engineering. We discuss several potential uses. In particular we highlight the role of recommendation systems in online forums that are used for capturing and discussing feature requests. The recommendation system is used to mitigate problems introduced when face-toface communication is replaced with potentially high-volume online discussions. In this context, recommendation systems can be used to suggest relevant topics to stakeholders and conversely to recommend expert stakeholders for each discussion topic. We also explore the use of recommendation systems in the domain analysis process, where they can be used to recommend sets of features to include in new products.

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Section: Recommendation Systems In Requirements Engineeringmentioning

confidence: 99%

Recommendation Systems in Requirements Discovery

Hariri

Castro-Herrera

Cleland‐Huang

et al. 2013

Recommendation Systems in Software Engineering

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show abstract

“…Work in recommendation systems includes: defining recommendations for new domains, such as requirements elicitation [5] and team communication [17]; frameworks for defining recommendation systems [10]; and techniques for choosing recommendations to include in a system, such as data mining [9].…”

Section: Related Workmentioning

confidence: 99%

Speculative analysis of integrated development environment recommendations

et al. 2012

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Modern integrated development environments make recommendations and automate common tasks, such as refactorings, auto-completions, and error corrections. However, these tools present little or no information about the consequences of the recommended changes. For example, a rename refactoring may: modify the source code without changing program semantics; modify the source code and (incorrectly) change program semantics; modify the source code and (incorrectly) create compilation errors; show a name collision warning and require developer input; or show an error and not change the source code. Having to compute the consequences of a recommendation -either mentally or by making source code changes -puts an extra burden on the developers. This paper aims to reduce this burden with a technique that informs developers of the consequences of code transformations. Using Eclipse Quick Fix as a domain, we describe a plug-in, Quick Fix Scout, that computes the consequences of Quick Fix recommendations. In our experiments, developers completed compilation-error removal tasks 10% faster when using Quick Fix Scout than Quick Fix, although the sample size was not large enough to show statistical significance.

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“…(Sarma & Herbsleb, 2008b;Sarma, Maccherone, Wagstrom, & Herbsleb, 2009); while Ariadne (de Souza, Quirk, Trainer, & Redmiles, 2007) matches similar developers based on the socio-technical dependencies. Ensemble (Xiang et al, 2008) is a recommendation tool that matches developers to artifacts while Palantir (Sarma, Noroozi, & van der Hoek, 2003;Sarma, Redmiles, & van der Hoek, 2008 and Hipikat (Cubranic & Murphy, 2003;Cubranic, Murphy, Singer, & Booth, 2005) can be used to understand workspace awareness. ProxiScientia can be used for real-time visualization of coordination needs (Borici, Blincoe, Schröter, Valetto, & Damian, 2012).…”

Section: Existing Stc Toolsmentioning

confidence: 99%

Engaging developers in open source software projects: harnessing social and technical data mining to improve software development

Carlson

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Ensemble

Cited by 19 publications

References 10 publications

Recommendation Systems in Requirements Discovery

Recommendation Systems in Requirements Discovery

Speculative analysis of integrated development environment recommendations

Engaging developers in open source software projects: harnessing social and technical data mining to improve software development

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