Machine Learning for Software Engineering: A Systematic Mapping

Shafiq, Saad; Mashkoor, Atif; Mayr-Dorn, Christoph; Egyed, Alexander

doi:10.48550/arxiv.2005.13299

Cited by 4 publications

(4 citation statements)

References 54 publications

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“…They conclude that software quality is the most frequent SWEBOK knowledge area target for both clusters (51%) and that ML is mainly used to make predictions in SE. A similar study by Shafiq et al 46 shows that based on 227 articles about ML for software engineering from 1991 to 2019, 21 were focusing on requirements, 39 on architecture and design, 21 on implementation, 119 on quality assurance and analytics and 9 on maintenance. Our study is richer and broader in the sense that we do not only focus on assistants empowered with ML techniques, and more specific in the sense that we are only interested in working assistants and ignore theoretical and unimplemented approaches.…”

Section: Previous Secondary Studies On Software Assistants In Sementioning

confidence: 85%

Software assistants in software engineering: A systematic mapping study

et al. 2022

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The increasing essential complexity of software systems makes current software engineering methods and practices fall short in many occasions. Software assistants have the ability to help humans achieve a variety of tasks, including the development of software. Such assistants, which show human‐like competences such as autonomy and intelligence, help software engineers do their job by empowering them with new knowledge. This article investigates the research efforts that have been conducted on the creation of assistants for software design, construction and maintenance paying special attention to the user‐assistant interactions. To this end, we followed the standard systematic mapping study method to identify and classify relevant works in the state of the art. Out of the 7580 articles resulting from the automatic search, we identified 112 primary studies that present works which qualify as software assistants. We provide all the resources needed to reproduce our study. We report on the trends and goals of the assistants, the tasks they perform, how they interact with users, the technologies and mechanisms they exploit to embed intelligence and provide knowledge, and their level of automation. We propose a classification of software assistants based on interactions and present an analysis of the different automation patterns. As outcomes of our study, we provide a classification of software assistants dealing with the design, construction and maintenance phases of software development, we discuss the results, identify open lines of work and challenges and call for new innovative and rigorous research efforts in this field.

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Section: Previous Secondary Studies On Software Assistants In Sementioning

confidence: 85%

Software assistants in software engineering: A systematic mapping study

et al. 2022

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“…QA1 QA2 QA3 QA4 Total P1 Abdellatif et al [16] 1 0 0 1 2 P2 Khan et al [17] 1 1 1 0.5 3.5 P3 Shaikh et al [18] 0 1.5 1 0 2.5 P4 Zarembo et al [19] 0.5 0.5 0.5 0.5 2 P5 Cao et al [20] 1 0.5 0 0 1.5 P6 Yang et al [21] 1 0.5 1 0.5 3 P7 Cote et al [22] 1 1 1 1 4 P8 Xu et al [23] 0.5 1 1 0.5 3 P9 Dey et al [24] 1 1 0.5 0 2.5 P10 Zanca et al [25] 1 0 1 1 3 P11 Al-Dasuqi et al [26] 0.5 1 0.5 0.5 2.5 P12 O'Hare et al [27] 1 1 1 0.5 3.5 P13 Druffel et al [28] 0 0 1 1 2 P14 Perkusich et al [29] 0.5 1 1 1 3.5 P15 Vinsard et al [30] 0.5 0 0.5 1 2 P16 Beegle et al [31] 1 0 1 0.5 2.5 P17 Salahirad et al [32] 1 1 0.5 0.5 3 P18 Dlamini et al [33] 0.5 0.5 0.5 0.5 2 P19 Cross et al [34] 0.5 1 1 1 3.5 P20 Malamateniou et al [35] 0 0 1 1 2 P21 Lenarduzzi et al [7] 1 0.5 0.5 0.5 2.5 P22 Sikorska et al [36] 0 1 0 1 2 P23 Field et al [37] 1 1 0 1 3 P24 Gao et al [38] 0.5 0 0 0 0.5 P25 Khanagar et al [39] 1 0 0 1 2 P26 Shafiq et al [40] 1 0.5 1 1 3.5 P27 Waade et al [41] 1 1 1 0.5 3.5 P28 Felderer et al [42] 1 0 0 0 1 P29 Ji et al [43] 0 0 1 1 2 P30 Harman et al [44] 1 0.5 1 1 3.5 P31 Shehab et al [45] 0 0.5 0.5 0.5 1.5 P32 Rana et al [46] 1 1 1 0 3 P33 Huang et al [47] 1 0.5 0.5 0.5 2.5 P34 Shakeel et al [48] 1 0.5 1 1 3.5 P35 Zhou et al [49] 0.5 0.5 0 0 1 P36 Sayago-Heredia et al…”

Section: Appendix Scores -Quality Assessmentmentioning

confidence: 99%

Taxonomy of Quality Assessment for Intelligent Software Systems: A Systematic Literature Review

Jabborov,

Kharlamova,

Kholmatova

et al. 2023

IEEE Access

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The increasing integration of AI software into various aspects of our daily lives has amplified the importance of evaluating the quality of these intelligent systems. The rapid proliferation of AI-based software projects and the growing reliance on these systems underscore the urgency of examining their quality for practical applications in both industry and academia. This systematic literature review delves into the study of quality assessment metrics and methods for AI-based systems, pinpointing key attributes and properties of intelligent software projects that are crucial for determining their quality. Furthermore, a comprehensive analysis of this domain will enable researchers to devise novel methods and metrics for effectively and efficiently evaluating the quality of such systems. Despite its importance, this area of development is still relatively nascent and evolving. This paper presents a systematic review of the current state of the taxonomy of quality assessment for AIbased software. We analyzed 271 articles from six different sources that focused on the quality assessment of intelligent software systems. The primary objective of this work is to provide an overview of the field and consolidate knowledge, which will aid researchers in identifying additional areas for future research. Moreover, our findings reveal the necessity to establish remedial strategies and develop tools to automate the process of identifying appropriate actions in response to abnormal metric values.

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“…Zhang et al [3] provides a comprehensive survey on ML testing considering 138 related papers on four aspects of MLSA testing such as what properties to test, what ML components to test, testing workflows and test scenario of different type of ML application. Shafiq et al [13] conducts a bibliometric study and provides an MLSA taxonomy of the adaptation of ML techniques across different SDLC phase. It focuses on the research attention at different SDLC of MLSA.…”

Section: Background and Related Workmentioning

confidence: 99%

Quality Assurance Challenges For Machine Learning Software Applications During Software Development Life Cycle Phases

Alamin

Uddin

2021

2021 IEEE International Conference on Autonomous Systems (ICAS)

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In the past decades, the revolutionary advances of Machine Learning (ML) have shown a rapid adoption of ML models into software systems of diverse types. Such Machine Learning Software Applications (MLSAs) are gaining importance in our daily lives. As such, the Quality Assurance (QA) of MLSAs is of paramount importance. Several research efforts are dedicated to determining the specific challenges we can face while adopting ML models into software systems. However, we are aware of no research that offered a holistic view of the distribution of those ML quality assurance challenges across the various phases of software development life cycles (SDLC). This paper conducts an in-depth literature review of a large volume of research papers that focused on the quality assurance of ML models. We developed a taxonomy of MLSA quality assurance issues by mapping the various ML adoption challenges across different phases of SDLC. We provide recommendations and research opportunities to improve SDLC practices based on the taxonomy. This mapping can help prioritize quality assurance efforts of MLSAs where the adoption of ML models can be considered crucial.

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Machine Learning for Software Engineering: A Systematic Mapping

Cited by 4 publications

References 54 publications

Software assistants in software engineering: A systematic mapping study

Software assistants in software engineering: A systematic mapping study

Taxonomy of Quality Assessment for Intelligent Software Systems: A Systematic Literature Review

Quality Assurance Challenges For Machine Learning Software Applications During Software Development Life Cycle Phases

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