Test Case Prioritization of Configurable Cyber-Physical Systems with Weight-Based Search Algorithms

Arrieta, Aitor; Wang, Shuai; Sagardui, Goiuria; Etxeberria, Leire

doi:10.1145/2908812.2908871

Cited by 25 publications

(20 citation statements)

References 17 publications

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“…There are search-based approaches for multi-objective test case prioritization in product lines (e.g., [10,11,81,112]). For instance, Parejo et al [81] model test case prioritization as a multi-objective optimization problem and implement a searchbased algorithm to solve it based on the NSGA-II evolutionary algorithm.…”

Section: Test Case Prioritizationmentioning

confidence: 99%

Automating system test case classification and prioritization for use case-driven testing in product lines

et al. 2020

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Product Line Engineering (PLE) is a crucial practice in many software development environments where software systems are complex and developed for multiple customers with varying needs. At the same time, many development processes are use case-driven and this strongly influences their requirements engineering and system testing practices. In this paper, we propose, apply, and assess an automated system test case classification and prioritization approach specifically targeting system testing in the context of use case-driven development of product families. Our approach provides: (i) automated support to classify, for a new product in a product family, relevant and valid system test cases associated with previous products, and (ii) automated prioritization of system test cases using multiple risk factors such as fault-proneness of requirements and requirements volatility in a product family. Our evaluation was performed in the context of an industrial product family in the automotive domain. Results provide empirical evidence that we propose a practical and beneficial way to classify and prioritize system test cases for industrial product lines.

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Section: Test Case Prioritizationmentioning

confidence: 99%

Automating system test case classification and prioritization for use case-driven testing in product lines

et al. 2020

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“…For instance, Zhang et al [3] defined a fitness function with three objectives (i.e., Block, Decision and Statement Coverage) and integrated the fitness function with hill climbing and GA for test case prioritization. Arrieta et al [7] proposed to prioritize test cases by defining a twoobjective fitness function (i.e., test case execution time and fault detection capability) and evaluated the performance of several search algorithms. The authors of [7] also proposed a strategy to give higher importance to test cases with higher positions (to be executed earlier).…”

Section: Related Workmentioning

confidence: 99%

“…Arrieta et al [7] proposed to prioritize test cases by defining a twoobjective fitness function (i.e., test case execution time and fault detection capability) and evaluated the performance of several search algorithms. The authors of [7] also proposed a strategy to give higher importance to test cases with higher positions (to be executed earlier). A number of research papers have focused on addressing the test case prioritization problem within a limited budget (e.g., time and test resource) using search-based approaches.…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate STIPI, we chose five approaches for the comparison: 1) Random Search (RS) to assess the complexity of the problem; 2) Greedy approach; 3) One existing approach [7] and two modified approaches from the existing literature [8,9]. The evaluation uses in total 211 test cases from Cisco, which are divided into three sets with varying complexity.…”

Section: Introductionmentioning

confidence: 99%

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STIPI: Using Search to Prioritize Test Cases Based on Multi-objectives Derived from Industrial Practice

Pradhan

Wang

Ali

et al. 2016

Testing Software and Systems

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The importance of cost-effectively prioritizing test cases is undeniable in automated testing practice in industry. This paper focuses on prioritizing test cases developed to test product lines of Video Conferencing Systems (VCSs) at Cisco Systems, Norway. Each test case requires setting up configurations of a set of VCSs, invoking a set of test APIs with specific inputs, and checking statuses of the VCSs under test. Based on these characteristics and available information related with test case execution (e.g., number of faults detected), we identified that the test case prioritization problem in our particular context should focus on achieving high coverage of configurations, test APIs, statuses, and high fault detection capability as quickly as possible. To solve this problem, we propose a search-based test case prioritization approach (named STIPI) by defining a fitness function with four objectives and integrating it with a widely applied multi-objective optimization algorithm (named Non-dominated Sorting Genetic Algorithm II). We compared STIPI with random search (RS), Greedy algorithm, and three approaches adapted from literature, using three real sets of test cases from Cisco with four time budgets (25%, 50%, 75% and 100%). Results show that STIPI significantly outperformed the selected approaches and managed to achieve better performance than RS for on average 39.9%, 18.6%, 32.7% and 43.9% for the coverage of configurations, test APIs, statuses and fault detection capability, respectively.

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“…It has since been implemented into tools to generate test data for C programs (e.g., IGUANA [17] and AUSTIN [14,15]); generate Java test suites with EvoSuite [3,4]; create relational database data with the SchemaAnalyst tool [9,18]; and combined with dynamic symbolic execution in Microsoft's Pex tool [16]. The AVM has also found application to additional problems, including decision ordering for software product lines [22], balancing workload in requirements assignment [21], solving reliability-redundancy-allocation problems [20], as well as test case selection [19] and test suite prioritization [2]. Since Korel's original work, the AVM has been extended and improved for problems in SBSE: now it can handle more variable types, including fixed-point numbers [7] and strings [9,18], and can leverage new strategies proven to speed up the search for certain common types of objective function landscape [10,11].…”

Section: Introductionmentioning

confidence: 99%

AVMf: An Open-Source Framework and Implementation of the Alternating Variable Method

McMinn

Kapfhammer

2016

Search Based Software Engineering

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Abstract. The Alternating Variable Method (AVM) has been shown to be a fast and effective local search technique for search-based software engineering. Recent improvements to the AVM have generalized the representations it can optimize and have provably reduced its running time. However, until now, there has been no general, publicly-available implementation of the AVM incorporating all of these developments. We introduce AVMf , an object-oriented Java framework that provides such an implementation. AVMf is available from http://avmframework.org for configuration and use in a wide variety of projects.

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Test Case Prioritization of Configurable Cyber-Physical Systems with Weight-Based Search Algorithms

Cited by 25 publications

References 17 publications

Automating system test case classification and prioritization for use case-driven testing in product lines

Automating system test case classification and prioritization for use case-driven testing in product lines

STIPI: Using Search to Prioritize Test Cases Based on Multi-objectives Derived from Industrial Practice

AVMf: An Open-Source Framework and Implementation of the Alternating Variable Method

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