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ASE 2021
Sun 14 - Sat 20 November 2021 Australia
Wed 17 Nov 2021 09:00 - 09:20 at Koala - Analysis I Chair(s): Pavneet Singh Kochhar

Mutation analysis is a powerful dynamic approach that has many applications, such as measuring the quality of test suites or automatically locating fault. However, the inherent low scalability hampers its practical use. To accelerate mutation analysis, researchers propose approaches to reduce redundant executions. A family of fork-based approaches tries to share identical executions among mutants. Fork-based approaches carry all mutants in one process, and decide whether to fork new child-processes when reach a mutated statement. The mutants carried by the parent process are split into groups and distribute to different processes to finish remaining executions. However, existing fork-based approaches have two limitations: (1) the limited analysis scope on a single statement to compare and cluster mutants prevents their systems from detecting more equivalent mutants, and (2) the interpretation of the mutants and the runtime equivalence analysis introduce significant overhead.

In this paper, we present a novel fork-based mutation analysis approach WinMut, which (1) groups mutants in a scope of mutated statements and, (2) removes redundant computations inside interpreters. WinMut not only reduces the number of invoked processes, but also has a lower cost for executing a single process. Our experiments show that our approach can further accelerate mutation analysis with an average speedup of 9.52x on the top of the state-of-the-art fork-based approach, AccMut.

Wed 17 Nov

Displayed time zone: Hobart change

09:00 - 10:00
Faster Mutation Analysis with Fewer Processes and Smaller Overheads
Research Papers
Bo Wang Beijing Jiaotong University, Sirui Lu Peking University, Yingfei Xiong Peking University, Feng Liu Beijing Jiaotong University
FRUGAL: Unlocking Semi-supervised Learning for Software Analytics
Research Papers
Huy Tu North Carolina State University, USA, Tim Menzies North Carolina State University
Automatically Deciding on the Integration of Commits Based on Their Descriptions
Industry Showcase
Samuel Cristo da Fonseca Sidia R&D, Mateus C. Lucena Sidia R&D , Tiago M. Reis Sidia R&D, Pedro F. Cabral Sidia R&D, Walmir A. Silva Sidia R&D, Flavia de S. Santos Sidia R&D, Felipe T. Giuntini Sidia R&D, Juliano Sales Sidia R&D
SigRec: Automatic Recovery of Function Signatures in Smart Contracts
Journal-first Papers
Ting Chen University of Electronic Science and Technology of China, zihao li The Hong Kong Polytechnic Universituy, Xiapu Luo Hong Kong Polytechnic University, XiaoFeng Wang Indiana University Bloomington, Ting Wang Penn State University, Hongwei Li University of Electronic Science and Technology of China, Xiaosong Zhang University of Electronic Science and Technology of China
Link to publication DOI