Recommending People in Developers' Collaboration Network

Surian, Didi; Liu, Nian; Lo, David; Tong, Hanghang; Lim, Ee-Peng; Faloutsos, Christos

doi:10.1109/wcre.2011.53

Cited by 52 publications

(48 citation statements)

References 32 publications

(45 reference statements)

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“…While this operationalization refines the 6-month window recommended in [86] for understanding OSS project evolution, sensitivity analyses such as those conducted in [181] can help reason about the design decision more thoroughly. A related threat is our reliance on discussion and commit information to detect social links between devel-opers.…”

Section: Threats To Validitymentioning

confidence: 91%

“…Effective Size of Group Closely related to our work are approaches recommending who in the developer social group, including who should fix an incoming bug [6], who should mentor OSS newcomers [34], who should be working together [181], and who should awareness be attended to [60]. When making these recommendations and the like, how many developers to consider can be answered via the group size model that our work uses.…”

Section: Rate Of Returnmentioning

confidence: 99%

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Stakeholders' social interaction in requirements engineering of open source software

Bhowmik

2014

2014 IEEE 22nd International Requirements Engineering Conference (RE)

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In this work, we use the phrase "stakeholders' social interaction" to indicate interaction among stakeholders regarding the software system that takes place through some communication means, such as posting comments and artifacts over the issue tracking system.We investigate the influence of stakeholders' social interaction in different RE activities, in particular, requirements identification, creativity in RE, and requirements implementation of OSS systems. This research enables us to gain valuable insights to generate guidelines for enhancing software engineering practice in relevant areas.

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Section: Threats To Validitymentioning

confidence: 91%

Section: Rate Of Returnmentioning

confidence: 99%

Stakeholders' social interaction in requirements engineering of open source software

Bhowmik

2014

2014 IEEE 22nd International Requirements Engineering Conference (RE)

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“…To measure the performance of Sim TagCombine , we use top-K prediction accuracies, which follows some previous studies [21][22][23][24] . Top-K prediction accuracy is the percentage of questions in the test set where their ground truth similar questions are ranked in the top-k positions in the returned ranked lists of similar questions.…”

Section: How Can We Use the Tags Recommended By Tagcombine?mentioning

confidence: 99%

“…Besides linked question recommendation, the tags recommended by our TagCombine can potentially be used to solve other software engineering tasks. For example, we can potentially use the tags to improve the performance of duplicate question detection in Q&A web sites such as Stack Overflow, expert finding in Q&A web sites [20] , and developer recommendation for a new project in Freecode or SourceForge [21] . In the future, we plan to solve these problems better by leveraging our TagCombine.…”

Section: How Can We Use the Tags Recommended By Tagcombine?mentioning

confidence: 99%

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TagCombine: Recommending Tags to Contents in Software Information Sites

Wang

Xia

2015

J. Comput. Sci. Technol.

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Nowadays, software engineers use a variety of online media to search and become informed of new and interesting technologies, and to learn from and help one another. We refer to these kinds of online media which help software engineers improve their performance in software development, maintenance, and test processes as software information sites. In this paper, we propose TagCombine, an automatic tag recommendation method which analyzes objects in software information sites. TagCombine has three different components: 1) multi-label ranking component which considers tag recommendation as a multi-label learning problem; 2) similarity-based ranking component which recommends tags from similar objects; 3) tag-term based ranking component which considers the relationship between different terms and tags, and recommends tags after analyzing the terms in the objects. We evaluate TagCombine on four software information sites, Ask Different, Ask Ubuntu, Freecode, and Stack Overflow. On averaging across the four projects, TagCombine achieves recall@5 and recall@10 to 0.619 8 and 0.762 5 respectively, which improves TagRec proposed by Al-Kofahi et al. by 14.56% and 10.55% respectively, and the tag recommendation method proposed by Zangerle et al. by 12.08% and 8.16% respectively.

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Error leakage and wasted time: sensitivity and effort analysis of a requirements consistency checking process

Hayes

Antoniol

et al. 2016

J Software Evolu Process

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Several techniques are used by requirements engineering practitioners to address difficult problems such as specifying precise requirements while using inherently ambiguous natural language text and ensuring the consistency of requirements. Often, these problems are addressed by building processes/tools that combine multiple techniques where the output from 1 technique becomes the input to the next. While powerful, these techniques are not without problems.Inherent errors in each technique may leak into the subsequent step of the process. We model and study 1 such process, for checking the consistency of temporal requirements, and assess error leakage and wasted time. We perform an analysis of the input factors of our model to determine the effect that sources of uncertainty may have on the final accuracy of the consistency checking process. Convinced that error leakage exists and negatively impacts the results of the overall consistency checking process, we perform a second simulation to assess its impact on the analysts' efforts to check requirements consistency. We show that analyst's effort varies depending on the precision and recall of the subprocesses and that the number and capability of analysts affect their effort. We share insights gained and discuss applicability to other processes built of piped techniques.

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Recommending People in Developers' Collaboration Network

Cited by 52 publications

References 32 publications

Stakeholders' social interaction in requirements engineering of open source software

Stakeholders' social interaction in requirements engineering of open source software

TagCombine: Recommending Tags to Contents in Software Information Sites

Error leakage and wasted time: sensitivity and effort analysis of a requirements consistency checking process

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