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ICSE 2020
Wed 24 June - Thu 16 July 2020
Fri 10 Jul 2020 16:11 - 16:23 at Silla - A24-Testing and Debugging 4 Chair(s): Yijun Yu

Software testing is an essential part of the software lifecycle and requires a substantial amount of time and effort. It has been estimated that software developers spend close to 50% of their time on testing the code they write. For these reasons, a long standing goal within the research community is to (partially) automate software testing. While several techniques and tools have been proposed to automatically generate test methods, recent work has criticized the quality and usefulness of the assert statements they generate. Therefore, we employ a Neural Machine Translation (NMT) based approach called ATLAS (AuTomatic Learning of Assert Statements) to automatically generate meaningful assert statements for test methods. Given a test method and a focal method (i.e., the main method under test), ATLAS can predict a meaningful assert statement to assess the correctness of the focal method. We applied ATLAS to thousands of test methods from GitHub projects and it was able to predict the exact assert statement manually written by developers in 31% of the cases when only considering the top-1 predicted assert. When considering the top-5 predicted assert statements, ATLAS is able to predict exact matches in 50% of the cases. These promising results hint to the potential usefulness of our approach as (i) a complement to automatic test case generation techniques, and (ii) a code completion support for developers, who can benefit from the recommended assert statements while writing test code.

Conference Day
Fri 10 Jul

Displayed time zone: (UTC) Coordinated Universal Time change

16:05 - 17:05
A24-Testing and Debugging 4Technical Papers / New Ideas and Emerging Results / Journal First / Demonstrations at Silla
Chair(s): Yijun YuThe Open University, UK
16:05
6m
Talk
Manifold for Machine Learning AssuranceNIER
New Ideas and Emerging Results
Taejoon ByunUniversity of Minnesota, Sanjai RayadurgamUniversity of Minnesota
16:11
12m
Talk
On Learning Meaningful Assert Statements for Unit Test CasesTechnical
Technical Papers
Cody WatsonWashington and Lee University, Michele TufanoMicrosoft, Kevin MoranWilliam & Mary/George Mason University, Gabriele BavotaUniversità della Svizzera italiana, Denys PoshyvanykWilliam and Mary
Pre-print Media Attached
16:23
12m
Talk
TRADER: Trace Divergence Analysis and Embedding Regulation for Debugging Recurrent Neural NetworksArtifact ReusableTechnicalArtifact Available
Technical Papers
Guanhong TaoPurdue University, Shiqing MaRutgers University, Yingqi LiuPurdue University, USA, Qiuling XuPurdue University, Xiangyu ZhangPurdue University
Pre-print
16:35
3m
Talk
DeepMutation: A Neural Mutation ToolDemo
Demonstrations
Michele TufanoMicrosoft, Jason KimkoWilliam & Mary, Shiya WangWilliam & Mary, Cody WatsonWashington and Lee University, Gabriele BavotaUniversità della Svizzera italiana, Massimiliano Di PentaUniversity of Sannio, Denys PoshyvanykWilliam and Mary
Pre-print
16:38
8m
Talk
Specification Patterns for Robotic MissionsJ1
Journal First
Claudio MenghiUniversity of Luxembourg, Christos TsigkanosTU Vienna, Patrizio PelliccioneUniversity of L'Aquila and Chalmers | University of Gothenburg, Carlo GhezziPolitecnico di Milano, Thorsten BergerChalmers | University of Gothenburg
16:46
8m
Talk
ProXray: Protocol Model Learning and Guided Firmware AnalysisJ1
Journal First
Farhaan FowzeUniversity of Florida, Dave (Jing) TianPurdue University, Grant HernandezUniversity of Florida, Kevin ButlerUniv. Florida, Tuba YavuzUniversity of Florida
16:54
6m
Talk
Visual Sketching: From Image Sketches to CodeNIER
New Ideas and Emerging Results
Marcelo d'AmorimFederal University of Pernambuco, Rui AbreuInstituto Superior Técnico, U. Lisboa & INESC-ID, Carlos MelloFederal University of Pernambuco
Pre-print Media Attached