Developer Recommendation for Crowdsourced Software Development Tasks

Mao, Ke; Yang, Ye; Wang, Qing; Jia, Yunyi; Harman, Mark

doi:10.1109/sose.2015.46

Cited by 84 publications

(31 citation statements)

References 28 publications

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“…In such cases the developers cannot be directly scheduled but may be optimised using recommendation techniques to guide them to work on their most suitable tasks. Mao et al [259] employed content-based technique to recommend developers for crowdsourced software development tasks. The approach learns from historical task registration and winner records to automatically match tasks and developers.…”

Section: Planning and Schedulingmentioning

confidence: 99%

A survey of the use of crowdsourcing in software engineering

Mao

Capra

Harman

et al. 2017

Journal of Systems and Software

Self Cite

294

229

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Crowdsourcing can be used to support software engineering activities and research into these activities. In this paper we provide a comprehensive survey of the use of crowdsourcing to support software engineering activities (Crowdsourced Software Engineering), seeking to cover all literature on this topic. We describe the software engineering domains, tasks and applications for crowdsourcing and the platforms and stakeholders involved in realising Crowdsourced Software Engineering solutions. We also expose trends, issues and opportunities for Crowdsourced Software Engineering.Please cite as: Ke Mao, Licia Capra, Mark Harman and Yue Jia. A Survey of the Use of Crowdsourcing in SoftwareEngineering.

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Section: Planning and Schedulingmentioning

confidence: 99%

A survey of the use of crowdsourcing in software engineering

Mao

Capra

Harman

et al. 2017

Journal of Systems and Software

Self Cite

294

229

View full text Add to dashboard Cite

show abstract

“…Data grafting techniques are employed to bridge the two stages. Mao et al [24] proposed a content-based recommendation technique to automatically match tasks and developers. The approach learns particular interests from registration history and mines winner history to favor appropriate developers.…”

Section: Automatic Bug Classification In Software Engineeringmentioning

confidence: 99%

Using Knowledge Transfer and Rough Set to Predict the Severity of Android Test Reports via Text Mining

Guo

Chen

2017

Symmetry

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Abstract:Crowdsourcing is an appealing and economic solution to software application testing because of its ability to reach a large international audience. Meanwhile, crowdsourced testing could have brought a lot of bug reports. Thus, in crowdsourced software testing, the inspection of a large number of test reports is an enormous but essential software maintenance task. Therefore, automatic prediction of the severity of crowdsourced test reports is important because of their high numbers and large proportion of noise. Most existing approaches to this problem utilize supervised machine learning techniques, which often require users to manually label a large number of training data. However, Android test reports are not labeled with their severity level, and manual labeling is time-consuming and labor-intensive. To address the above problems, we propose a Knowledge Transfer Classification (KTC) approach based on text mining and machine learning methods to predict the severity of test reports. Our approach obtains training data from bug repositories and uses knowledge transfer to predict the severity of Android test reports. In addition, our approach uses an Importance Degree Reduction (IDR) strategy based on rough set to extract characteristic keywords to obtain more accurate reduction results. The results of several experiments indicate that our approach is beneficial for predicting the severity of android test reports.

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“…The second work it was developed by Mao et al [20] (SW02) and highlighted a model of recommendations for the development of CS activities. This work it was validated through experimentation and demonstrated practical results of its approach.…”

Section: IIImentioning

confidence: 99%

“…To initiate the analysis of the main reasons for failure, a similar approach it was applied to Mao et al [20]. In this approach, the microtasks were added in a computational vector.…”

Section: B What Are the Main Reasons For Failure Found In Microtasksmentioning

confidence: 99%