Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States

Modern software projects include automated tests written to check the programs’ functionality. The set of functions invoked by a test is called the trace of the test, and the action of obtaining a trace is called tracing. There are many tracing tools since traces are useful for a variety of software engineering tasks such as test generation, fault localization, and test execution planning. A major drawback in using test traces is that obtaining them, i.e., tracing, can be costly in terms of computational resources and runtime. Prior work attempted to address this in various ways, e.g., by selectively tracing only some of the software components or compressing the trace on-the-fly. However, all these approaches still require building the project and executing the test in order to get its (partial, possibly compressed) trace. This is still very costly in many cases. In this work, we propose a method to predict the trace of each test without executing it, based only on static properties of the test and the tested program, as well as past experience on different tests. This prediction is done by applying supervised learning to learn the relation between various static features of test and function and the likelihood that one will include the other in its trace. Then, we show how to use the predicted traces in a recent automated troubleshooting paradigm called Learn Diagnose and plan (LDP), instead of the actual, costly-to-obtain, test traces. In a preliminary evaluation on real-world open-source projects, we observe that our prediction quality is reasonable. In addition, using our trace predictions in LDP yields almost the same results comparing to when using real traces, while requiring less overhead.

Wed 13 Nov

ase-2019-Late-Breaking-Results
15:20 - 16:00: Late Breaking Results - Poster Session: Late Breaking Results at Kensington Ballroom
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Recommendation of Exception Handling Code in Mobile App Development Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
LVMapper: A Large-variance Clone Detector Using Sequencing Alignment Approach
Ming Wu, Pengcheng WangUniversity of Science and Technology of China, Kangqi Yin, Haoyu Cheng, Yun XuUniversity of Science and Technology of China, Chanchal K. RoyUniversity of Saskatchewan
Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
K-CONFIG: Using Failing Test Cases to Generate Test Cases in GCC Compilers Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
An Empirical Study on the Characteristics of Question-Answering Process on Developer Forums
Yi LiNanyang Technological University, Shaohua WangNew Jersey Institute of Technology, USA, Tien N. NguyenUniversity of Texas at Dallas, Son NguyenThe University of Texas at Dallas, Xinyue Ye, Yan Wang
Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Testing Neural Programs
Md Rafiqul Islam RabinUniversity of Houston, Ke WangVisa Research, Mohammad Amin Alipour
Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Self Learning from Large Scale Code Corpus to Infer Structure of Method Invocations Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Data Sanity Check for Deep Learning Systems via Learnt Assertions
Haochuan LuFudan University, Huanlin Xu, Nana Liu, Yangfan ZhouFudan University, Xin Wang
Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Software Engineering for Fairness: A Case Study with Hyperparameter Optimization
Joymallya ChakrabortyNorth Carolina State University, Tianpei Xia, Fahmid M. Fahid, Tim MenziesNorth Carolina State University
Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
API Misuse Correction: A Statistical Approach Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Should We Add Repair Time to an Unfixed Bug? An Exploratory Study of Automated Program Repair on 2980 Small-Scale Programs Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Learning test traces Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
The Dynamics of Software Composition Analysis Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
A Process Mining based Approach to Improving Defect Detection of SysML Models.
Mounifah Alenazi, Nan NiuUniversity of Cincinnati, Juha SavolainenDanfoss
Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Open-Source Projects and their Collaborative Development Workflows
panuchart bunyakiatikasetsart university, Usa Sammapunkasetsart university
Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Detecting Deep Neural Network Defects with Data Flow Analysis
Jiazhen Gu, Huanlin Xu, Yangfan ZhouFudan University, Xin Wang, Hui Xu, Michael LyuThe Chinese University of Hong Kong
Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
On building an automated responding system for app reviews: What are the characteristics of reviews and their responses? Pre-print