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ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Wed 13 Nov 2019 14:00 - 14:20 at Hillcrest - Configurations and Variability Chair(s): Shin Hwei Tan

Nowadays software tends to come in many different, yet similar variants, often derived from a common codebase via clone-and-own. Family-based-analysis strategies have recently shown very promising potential for improving efficiency in applying quality-assurance techniques to such variant-rich programs, as compared to variant-by-variant approaches. Unfortunately, these strategies require a single program representation superimposing all program variants in a syntactically well-formed, semantically sound and variant-preserving manner, which is usually not available and manually hard to obtain in practice. In this paper, we present a novel methodology, called SiMPOSE, for automatically generating superimpositions of existing program variants to facilitate family-based analyses of variant-rich software. To this end, we propose a novel N-way model-merging methodology to integrate the control-flow automaton (CFA) representations of N given variants of a C program into one unified CFA representation. CFA constitute a unified program abstraction used by many recent software-analysis tools for automated quality assurance. To cope with the inherent complexity of N-way model-merging, our approach (1) utilizes principles of similarity-propagation to reduce the number of potential N-way matches, and (2) enables us to decompose a set of N variants into arbitrary subsets and to incrementally derive an N-way superimposition from partial superimpositions. We apply our tool implementation of SiMPOSE to a selection of realistic C programs, frequently considered for experimental evaluation of program-analysis techniques. In particular, we investigate applicability and efficiency/effectiveness trade-offs of our approach by applying SiMPOSE in the context of family-based unit-test generation as well as model-checking as sample program-analysis techniques. Our experimental results reveal very impressive efficiency improvements by an average factor of up to 2.6 for test-generation and up to 2.4 for model-checking under stable effectiveness, as compared to variant-by-variant approaches, thus amortizing the additional effort required for merging. In addition, our results show that merging all N variants at once produces, in almost all cases, clearly more precise results than incremental step-wise 2-way merging. Finally, our comparison with major existing N-way merging techniques shows that SiMPOSE constitutes, in most cases, the best efficiency/effectiveness trade-off.

Wed 13 Nov

ase-2019-paper-presentations
13:40 - 15:20: Papers - Configurations and Variability at Hillcrest
Chair(s): Shin Hwei TanSouthern University of Science and Technology
ase-2019-papers13:40 - 14:00
Talk
Liang BaoSchool of Computer Science and Technology, XiDian University, Xin LiuDepartment of Computer Science, University of California, Davis, Fangzheng WangSchool of Computer Science and Technology, XiDian University, Baoyin FangSchool of Computer Science and Technology, XiDian University
ase-2019-Journal-First-Presentations14:00 - 14:20
Talk
Dennis ReulingSoftware Engineering Group, University of Siegen, Udo KelterSoftware Engineering Group, University of Siegen, Johannes BürdekTU Darmstadt, Real-time Systems Lab, Malte LochauTU Darmstadt
Link to publication DOI
ase-2019-papers14:20 - 14:40
Talk
Eric HortonNorth Carolina State University, Chris ParninNCSU
Pre-print
ase-2019-papers14:40 - 15:00
Talk
Son NguyenThe University of Texas at Dallas, Hoan Anh NguyenAmazon, Ngoc TranUniversity of Texas at Dallas, Hieu TranThe University of Texas at Dallas, Tien N. NguyenUniversity of Texas at Dallas
ase-2019-Journal-First-Presentations15:00 - 15:20
Talk
Dipesh PradhanSimula Research Laboratory, Norway, Shuai WangHong Kong University of Science and Technology, Tao YueNanjing University of Aeronautics and Astronautics & Simula Research Laboratory, Shaukat AliSimula Research Lab, Marius LiaaenCisco Systems
Link to publication