Regression Test Suites Optimization for Application-specific Instruction-Set Processors and Their Use for Dependability Analysis

Zachariaova, Marcela; Kekelyova-Beleova, Michaela; Kotásek, Zdenĕk

doi:10.1109/dsd.2016.50

Cited by 2 publications

(1 citation statement)

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“…Empirical assessment done on 346 java classes shows that DynaMOSA holds substantial improvements with respect to statement, branch and mutation coverage as compared to whole suite approach and its antecedent MOSA. Zachariáová et al [31] employ the genetic algorithm in order to build a new approach for regression suite optimization of Application-specific Instruction-Set Processors (ASIPs). According to simulation results generated by QuestaSim tool, this optimization technique reduces the simulation runtime and the resulting statistics for coverage remains equal to actual suite coverage.…”

Section: B Meta-heuristic Algorithm Categorymentioning

confidence: 99%

A Comprehensive Investigation of Modern Test Suite Optimization Trends, Tools and Techniques

et al. 2019

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Software testing is an important but expensive activity of software development life cycle, as it accounts for more than 52% of entire development cost. Testing requires the execution of all possible test cases in order to find the defects in the software. Therefore, different test suite optimization approaches like the genetic algorithm and the greedy algorithm, etc., are widely used to select the representative test suite without compromising the effectiveness. Test suite optimization is frequently researched to enhance its competences but there is no study published until now that analyzes the latest developments from 2016 to 2019. Hence, in this article, we systematically examine the state-of-the-art optimizations' approaches, tools, and supporting platforms. Principally, we conducted a systematic literature review (SLR) to inspect and examine 58 selected studies that are published during 2016-2019. Subsequently, the selected researches are grouped into five main categories, i.e., greedy algorithm (seven studies), meta-heuristic (28 studies), hybrid (six studies), clustering (five studies), and general (12 studies). Finally, 32 leading tools have been presented, i.e., existing tools (25 tools) and proposed/developed tools (seven tools) along 14 platform supports. Furthermore, it is noted that several approaches aim at solving the single-objective optimization problem. Therefore, researchers should focus on dealing with the multi-objective problem, as multi-objective versions outperform the single-objective ones. Moreover, less attention has been given to clustering-based techniques. Thus, we recommend exploring the machine learning and artificial intelligencebased optimization approaches in the future. A broad exploration of tools and techniques, in this article, will help researchers, practitioners, and developers to opt for adequate techniques, tools, or platforms as per requirements.

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Section: B Meta-heuristic Algorithm Categorymentioning

confidence: 99%