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ICSE 2020
Wed 24 June - Thu 16 July 2020
Tue 7 Jul 2020 08:13 - 08:25 at Silla - I6-Empirical Studies and Requirements Chair(s): Ita Richardson

Online chatting is gaining popularity and plays an increasingly significant role in software development. When discussing functionalities, developers might reveal their desired features to other developers. Automated mining techniques towards retrieving feature requests from massive chat messages can benefit the requirements gathering process. But it is quite challenging to perform such techniques because detecting feature requests from dialogues requires a thorough understanding of the contextual information, and it is also extremely expensive on annotating feature-request dialogues for learning. To bridge that gap, we recast the traditional text classification task of mapping single dialog to its class into the task of determining whether two dialogues are similar or not by incorporating few-shot learning. We propose a novel approach, named FRMiner, which can detect feature-request dialogues from chat messages via deep Siamese network. We design a BiLSTM-based dialog model that can learn the contextual information of a dialog in both forward and reverse directions. Evaluation on the real-world projects shows that our approach achieves average precision, recall and F1-score of 88.52%, 88.50% and 88.51%, which confirm that our approach could effectively detect hidden feature requests from chat messages, thus can facilitate gathering comprehensive requirements from the crowd in an automated way.

Tue 7 Jul
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08:05 - 09:05: I6-Empirical Studies and RequirementsPaper Presentations / Journal First / Software Engineering in Practice / Technical Papers at Silla
Chair(s): Ita RichardsonLero - The Irish Software Research Centre and University of Limerick
08:05 - 08:13
What do Programmers Discuss about Deep Learning FrameworksJ1
Journal First
Junxiao HanZhejiang University, Emad ShihabConcordia University, Zhiyuan WanZhejiang University, Shuiguang DengZhejiang University, Xin XiaMonash University
08:13 - 08:25
Detection of Hidden Feature Requests from Massive Chat Messages via Deep Siamese NetworkTechnicalArtifact Available
Technical Papers
Lin ShiISCAS, Mingzhe XingISCAS, Mingyang LiISCAS, Yawen WangISCAS, Shoubin LiISCAS, Qing WangInstitute of Software, Chinese Academy of Sciences
08:25 - 08:33
Recognizing lines of code violating company-specific coding guidelines using machine learningJ1
Journal First
Miroslaw OchodekPoznan University of Technology, Regina HebigChalmers University of Technology & University of Gothenburg, Wilhelm MedingEricsson, Gert FrostGrundfos, Miroslaw StaronUniversity of Gothenburg
08:33 - 08:45
Context-aware In-process Crowdworker RecommendationACM SIGSOFT Distinguished Paper AwardsTechnical
Technical Papers
Junjie WangInstitute of Software, Chinese Academy of Sciences, Ye YangStevens institute of technology, Song WangYork University, Yuanzhe HuInstitute of Software, Chinese Academy of Sciences, Dandan WangInstitute of Software, Chinese Academy of Sciences, Qing WangInstitute of Software, Chinese Academy of Sciences
08:45 - 08:57
Using a Context-Aware Approach to Recommend Code Reviewers: Findings from an Industrial Case StudySEIP
Software Engineering in Practice
Anton StrandEricsson AB, Markus GunnarssonEricsson AB, Ricardo BrittoEricsson / Blekinge Institute of Technology, Muhammad UsmanBlekinge Institute of Technology