A Systematic Review of Interaction in Search-Based Software Engineering

Ramírez, Aurora; Romero, José Raúl; Simons, Chris

doi:10.1109/tse.2018.2803055

Cited by 39 publications

(34 citation statements)

References 88 publications

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“…The ranges of the input test data in this paper are selected from previous work as (0–100), (0–200), and (0–500) [ 29 ]. The results show that the increase in test input data range will gradually decrease the path coverage percentage, increase the number of test data, and generation number required to fulfill all program paths.…”

Section: Resultsmentioning

confidence: 99%

“…The fitness function evaluates how good a solution is in order to achieve the search goal. The value of an individual fitness represents the measure of its ability to survive in the following generations [ 4 , 29 ]. The essential role of the fitness function is to compel the search algorithm to spend more effort on the branches that have higher weights based on the priority of the program paths or the most critical paths in the program.…”

Section: Related Workmentioning

confidence: 99%

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Optimal path test data generation based on hybrid negative selection algorithm and genetic algorithm

2020

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Path testing is the basic approach of white box testing and the main approach to solve it by discovering the particular input data of the searching space to encompass the paths in the software under test. Due to the increasing software complexity, exhaustive testing is impossible and computationally not feasible. The ultimate challenge is to generate suitable test data that maximize the coverage; many approaches have been developed by researchers to accomplish path coverage. The paper suggested a hybrid method (NSA-GA) based on Negative Selection Algorithm (NSA) and Genetic Algorithm (GA) to generate an optimal test data avoiding replication to cover all possible paths. The proposed method modifies the generation of detectors in the generation phase of NSA using GA, as well as, develops a fitness function based on the paths’ prioritization. Different benchmark programs with different data types have been used. The results show that the hybrid method improved the coverage percentage of the programs’ paths, even for complicated paths and its ability to minimize the generated number of test data and enhance the efficiency even with the increased input range of different data types used. This method improves the effectiveness and efficiency of test data generation and maximizes search space area, increasing percentage of path coverage while preventing redundant data.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Optimal path test data generation based on hybrid negative selection algorithm and genetic algorithm

2020

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“…Multi-objective optimization problems in Search-Based Software Engineering (SBSE) [18] are common (e.g., release planning, requirements prioritization, and test optimization [1,19,25,27,33]). Evidence shows that the applications of Multi-Objective Search Algorithms (MOSAs) to solve such SBSE problems have increased significantly [46].…”

Section: Introductionmentioning

confidence: 99%

Quality Indicators in Search-based Software Engineering

Ali

Arcaini

Pradhan

et al. 2020

ACM Trans. Softw. Eng. Methodol.

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Search-Based Software Engineering (SBSE) researchers who apply multi-objective search algorithms (MOSAs) often assess the quality of solutions produced by MOSAs with one or more quality indicators (QIs). However, SBSE lacks evidence providing insights on commonly used QIs, especially about agreements among them and their relations with SBSE problems and applied MOSAs. Such evidence about QIs agreements is essential to understand relationships among QIs, identify redundant QIs, and consequently devise guidelines for SBSE researchers to select appropriate QIs for their specific contexts. To this end, we conducted an extensive empirical evaluation to provide insights on commonly used QIs in the context of SBSE, by studying agreements among QIs with and without considering differences of SBSE problems and MOSAs. In addition, by defining a systematic process based on three common ways of comparing MOSAs in SBSE, we present additional observations that were automatically produced based on the results of our empirical evaluation. These observations can be used by SBSE researchers to gain a better understanding of the commonly used QIs in SBSE, in particular, regarding their agreements. Finally, based on the results, we also provide a set of guidelines for SBSE researchers to select appropriate QIs for their particular context.

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“…Ultimately, due to the low cost of building software, formal methods for engineering validation are not used widely in software development for real implementation. Software development is considered a profession more than an engineering exercise due to a lack of firmness in the fundamental processes of design improvement and validation (Ramirez et al, 2018).…”

Section: Introductionmentioning

confidence: 99%