A Preliminary Systematic Mapping Study of Human Competitiveness of SBSE

Souza, Jerffeson; Araújo, Allysson Allex; Saraiva, Raphael; Soares, Pamella; Maia, Camila Loiola Brito

doi:10.1007/978-3-319-99241-9_6

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…It is worth considering that in the accumulated literature, we found a number of review studies targeted on specific contexts of SBSE, such as different areas in testing, requirements, design, and software refactoring, and many others, but their results may not be generalizable and address little attention to the broader collection of literature in this field, especially in performance metrics. In this regard, to utilize our limited scope, readers are redirected to references [22][23][24][25][26][27][28][29][30][31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation Metrics for Multi-Objective Evolutionary Algorithms in Search-Based Software Engineering: Systematic Literature Review

et al. 2021

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Many recent studies have shown that various multi-objective evolutionary algorithms have been widely applied in the field of search-based software engineering (SBSE) for optimal solutions. Most of them either focused on solving newly re-formulated problems or on proposing new approaches, while a number of studies performed reviews and comparative studies on the performance of proposed algorithms. To evaluate such performance, it is necessary to consider a number of performance metrics that play important roles during the evaluation and comparison of investigated algorithms based on their best-simulated results. While there are hundreds of performance metrics in the literature that can quantify in performing such tasks, there is a lack of systematic review conducted to provide evidence of using these performance metrics, particularly in the software engineering problem domain. In this paper, we aimed to review and quantify the type of performance metrics, number of objectives, and applied areas in software engineering that reported in primary studies—this will eventually lead to inspiring the SBSE community to further explore such approaches in depth. To perform this task, a formal systematic review protocol was applied for planning, searching, and extracting the desired elements from the studies. After considering all the relevant inclusion and exclusion criteria for the searching process, 105 relevant articles were identified from the targeted online databases as scientific evidence to answer the eight research questions. The preliminary results show that remarkable studies were reported without considering performance metrics for the purpose of algorithm evaluation. Based on the 27 performance metrics that were identified, hypervolume, inverted generational distance, generational distance, and hypercube-based diversity metrics appear to be widely adopted in most of the studies in software requirements engineering, software design, software project management, software testing, and software verification. Additionally, there are increasing interest in the community in re-formulating many objective problems with more than three objectives, yet, currently are dominated in re-formulating two to three objectives.

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Section: Related Workmentioning

confidence: 99%

Performance Evaluation Metrics for Multi-Objective Evolutionary Algorithms in Search-Based Software Engineering: Systematic Literature Review

et al. 2021

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A Systematic Mapping Study of Search-Based Software Engineering for Enterprise Application Integration

Mazzonetto¹,

Frantz²,

Roos‐Frantz³

et al. 2022

Int. J. Soft. Eng. Knowl. Eng.

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Search-Based Software Engineering (SBSE) is widely used in different fields of Software Engineering, notoriously, in Enterprise Application Integrations (EAIs). EAI encompasses methodologies, techniques and tools that a software engineer can use to create integration solutions. SBSE is currently an active research topic of increasing interest. The number and diversity of publications produced yearly are large to the extent that it is hard to identify the active research groups, their locations, techniques used and research topics that have not received enough attention. To answer these questions categorically, we have conducted systematic mapping study of the literature. In this paper, we report our methodology and findings. In our study, we used systematic search strategies that resulted in the retrieval of 560 articles, of which we first selected 25. Second, on the basis of the authors’ experience, we included eight additional articles. Finally, we used a snowballing sample technique to include another 12 articles. The results demonstrate that during the last two decades (1999–2020) EAI has benefited from the use of Search-Based Software Engineering techniques.

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A Preliminary Systematic Mapping Study of Human Competitiveness of SBSE

Cited by 2 publications

References 38 publications

Performance Evaluation Metrics for Multi-Objective Evolutionary Algorithms in Search-Based Software Engineering: Systematic Literature Review

Performance Evaluation Metrics for Multi-Objective Evolutionary Algorithms in Search-Based Software Engineering: Systematic Literature Review

A Systematic Mapping Study of Search-Based Software Engineering for Enterprise Application Integration

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