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ICSE 2020
Wed 24 June - Thu 16 July 2020
Tue 7 Jul 2020 08:33 - 08:45 at Silla - I6-Empirical Studies and Requirements Chair(s): Ita Richardson

Identifying and optimizing open participation is essential to the success of open software development. Existing studies highlighted the importance of worker recommendation for crowdtesting tasks in order to detect more bugs with fewer workers. However, these studies mainly provide one-time recommendations, i.e., recommending a set of workers for a new task with its fixed, initial context at the beginning of the task. We argue the need for in-process crowdtesting worker recommendation. We motivate this study through a pilot study, revealing the prevalence of long-sized non-yielding windows, i.e., no new bugs are revealed in consecutive test reports during the process of a crowdtesting task. This indicates the potential opportunity for accelerating test cycle by recommending appropriateworkers in a dynamic manner, so that the non-yielding windows could be shortened. This paper proposes a context-aware in-process crowdworker recommendation approach, R3Rec, to dynamically identify and rank a diverse set of capable crowdworkers, in order to detect more bugs earlier and potentially shorten the non-yielding windows. It consists of three main components: 1) the modeling of dynamic testing context in terms of process context and resource context to capture the in-process progress and worker characteristics respectively; 2) the learning-based ranking component to learn the probability of crowdworkers’ bug detection capability; and 3) the diversity-based re-ranking component to adjust the ranked list to potentially reduce the duplicate bugs. The evaluation is conducted on 636 crowdtesting tasks from one of the largest crowdtesting platforms, and the results showthat R3Rec can shorten the non-yielding windows by 50% -58% and reduce the total crowdtesting cost by about 10% on median. This is the first work to propose the in-process worker recommendation solution, and results showed its potential in improving the costeffectiveness of crowdtesting by saving the cost and shortening the testing cycle.

Tue 7 Jul

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08:05 - 09:05
I6-Empirical Studies and RequirementsJournal First / Software Engineering in Practice / Technical Papers at Silla
Chair(s): Ita Richardson Lero - The Irish Software Research Centre and University of Limerick
08:05
8m
Talk
What do Programmers Discuss about Deep Learning FrameworksJ1
Journal First
Junxiao Han Zhejiang University, Emad Shihab Concordia University, Zhiyuan Wan Zhejiang University, Shuiguang Deng Zhejiang University, Xin Xia Monash University
08:13
12m
Talk
Detection of Hidden Feature Requests from Massive Chat Messages via Deep Siamese NetworkTechnicalArtifact Available
Technical Papers
Lin Shi ISCAS, Mingzhe Xing ISCAS, Mingyang Li ISCAS, Yawen Wang ISCAS, Shoubin Li ISCAS, Qing Wang Institute of Software, Chinese Academy of Sciences
08:25
8m
Talk
Recognizing lines of code violating company-specific coding guidelines using machine learningJ1
Journal First
Miroslaw Ochodek Poznan University of Technology, Regina Hebig Chalmers University of Technology & University of Gothenburg, Wilhelm Meding Ericsson, Gert Frost Grundfos, Miroslaw Staron University of Gothenburg
08:33
12m
Talk
Context-aware In-process Crowdworker RecommendationACM SIGSOFT Distinguished Paper AwardsTechnical
Technical Papers
Junjie Wang Institute of Software, Chinese Academy of Sciences, Ye Yang Stevens institute of technology, Song Wang York University, Yuanzhe Hu Institute of Software, Chinese Academy of Sciences, Dandan Wang Institute of Software, Chinese Academy of Sciences, Qing Wang Institute of Software, Chinese Academy of Sciences
08:45
12m
Talk
Using a Context-Aware Approach to Recommend Code Reviewers: Findings from an Industrial Case StudySEIP
Software Engineering in Practice
Anton Strand Ericsson AB, Markus Gunnarsson Ericsson AB, Ricardo Britto Ericsson / Blekinge Institute of Technology, Muhammad Usman Blekinge Institute of Technology