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ICSE 2020
Wed 24 June - Thu 16 July 2020
Thu 9 Jul 2020 08:41 - 08:49 at Baekje - I16-Testing and Debugging 2 Chair(s): Rui Abreu

Developers usually depend on inserting logging statements into the source code to collect system runtime information. Such logged information is valuable for software maintenance. A logging statement usually prints one or more variables to record vital system status. However, due to the lack of rigorous logging guidance and the requirement of domain-specific knowledge, it is not easy for developers to make proper decisions about which variables to log. To address this need, in this paper [1], we propose an approach to recommend logging variables for developers during development by learning from existing logging statements. Different from other prediction tasks in software engineering, this task has two challenges: 1) Dynamic labels – different logging statements have different sets of accessible variables, which means in this task, the set of possible labels of each sample is not the same. 2) Out-of-vocabulary words – identifiers’ names are not limited to natural language words and the test set usually contains a number of program tokens which are out of the vocabulary built from the training set and cannot be appropriately mapped to word embeddings. To deal with the first challenge, we convert this task into a representation learning problem instead of a multi-label classification problem. Given a code snippet which lacks a logging statement, our approach first leverages a neural network with an RNN (recurrent neural network) layer and a self-attention layer to learn the proper representation of each program token, and then predicts whether each token should be logged through a unified binary classifier based on the learned representation. To handle the second challenge, we propose a novel method to map program tokens into word embeddings by making use of the pre-trained word embeddings of natural language tokens. We evaluate our approach on 9 large and high-quality Java projects. Our evaluation results show that the average MAP of our approach is over 0.84, outperforming random guess and an information-retrieval-based method by large margins.

Thu 9 Jul

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08:05 - 09:05
I16-Testing and Debugging 2Technical Papers / Journal First at Baekje
Chair(s): Rui Abreu Instituto Superior Técnico, U. Lisboa & INESC-ID
08:05
12m
Talk
Low-Overhead Deadlock PredictionTechnical
Technical Papers
Yan Cai Institute of Software, Chinese Academy of Sciences, Ruijie Meng University of Chinese Academy of Sciences, Jens Palsberg University of California, Los Angeles
08:17
8m
Talk
The Impact of Feature Reduction Techniques on Defect Prediction ModelsJ1
Journal First
Masanari Kondo Kyoto Institute of Technology, Cor-Paul Bezemer University of Alberta, Canada, Yasutaka Kamei Kyushu University, Ahmed E. Hassan Queen's University, Osamu Mizuno Kyoto Institute of Technology
08:25
8m
Talk
The Impact of Correlated Metrics on the Interpretation of Defect ModelsJ1
Journal First
Jirayus Jiarpakdee Monash University, Australia, Chakkrit Tantithamthavorn Monash University, Australia, Ahmed E. Hassan Queen's University
08:33
8m
Talk
The Impact of Mislabeled Changes by SZZ on Just-in-Time Defect PredictionJ1
Journal First
Yuanrui Fan Zhejiang University, Xin Xia Monash University, Daniel Alencar Da Costa University of Otago, David Lo Singapore Management University, Ahmed E. Hassan Queen's University, Shanping Li Zhejiang University
08:41
8m
Talk
Which Variables Should I Log?J1
Journal First
Zhongxin Liu Zhejiang University, Xin Xia Monash University, David Lo Singapore Management University, Zhenchang Xing Australia National University, Ahmed E. Hassan Queen's University, Shanping Li Zhejiang University
08:49
12m
Talk
Understanding the Automated Parameter Optimization on Transfer Learning for Cross-Project Defect Prediction: An Empirical StudyTechnicalArtifact Available
Technical Papers
Ke Li University of Exeter, Zilin Xiang University of Electronic Science and Technology of China, Tao Chen Loughborough University, Shuo Wang , Kay Chen Tan City University of Hong Kong
Pre-print