Adapting SQuaRE for Quality Assessment of Artificial Intelligence Systems

Kuwajima, Hiroshi; Ishikawa, Fuyuki

doi:10.1109/issrew.2019.00035

Cited by 31 publications

(12 citation statements)

References 9 publications

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“…Ghezzi et al expressed that interior characteristics manage the structure of programming, which helps the product engineers to accomplish the external features just as essential attributes of programming, which are accuracy, flexibility, integrity, practicality, portability, reliability, reusability, and convenience [55], [43].…”

Section: Discussionmentioning

confidence: 99%

“…Other quality attributes/subqualities are required to be included, which can cover new highlights of crosscutting concerns (aspect(s)) and the combination of it with essential concerns (classes). The redefinition of existing attributes/subqualities, in the setting of AO innovation, is likewise required [43], [44].…”

Section: Deutsch and Willsmentioning

confidence: 99%

“…SQuaRE comprises a group of principles under the general title of software product quality requirements and evaluation ( Fig. 8 represents the association of these families or divisions [41], [48], [43]. Fig.…”

Section: Deutsch and Willsmentioning

confidence: 99%

“…Fig. 2 -explains the architecture of the SQuaRE framework [41], [48], [43]. Adding more ISO/IEC 9126-1 and ISO/IEC 25010 it focus on software components is built to be agreeable with specific needs, required by its user.…”

Section: Deutsch and Willsmentioning

confidence: 99%

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empirical evaluation of metrics on aspect-oriented programs

Ghareb

Allen

2019

UHD J SCI TECH

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The quality evaluation of software metrics measurement is considered as the primary indicator of imperfection prediction and software maintenance in various empirical studies of software products. However, there is no agreement on which metrics are compelling quality pointers for new software development approaches such as aspect-oriented programming (AOP) techniques. AOP intends to enhance programming quality by providing fundamentally different parts of the systems, for example, pointcuts, advice, and intertype relationships. Hence, it is not evident if quality characteristics for AOP could be extracted from direct expansions of traditional object-oriented programming (OOP) measurements. Then again, investigations of AOP do regularly depend on established static and dynamic metrics measurement; notwithstanding the late research of AOP in empirical studies, few analyses been adopted using the International Organization for Standardization 9126 quality model as useful markers of flaw inclination in this context. This paper examination we have considered different programming quality models given by various authors every once in a while and distinguished that adaptability was deficient in the current model. We have testing 10 projects developed by AOP. We have used many applications to extract the metrics, but none of them could extract all AOP Metrics. It only can measure some of AOP Metrics, not all of them. This study investigates the suitable framework for extract AOP Metrics, for instance, static and dynamic metrics measurement for hybrid application systems (AOP and OOP) or only AOP application.

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Section: Discussionmentioning

confidence: 99%

Section: Deutsch and Willsmentioning

confidence: 99%

Section: Deutsch and Willsmentioning

confidence: 99%

Section: Deutsch and Willsmentioning

confidence: 99%

See 2 more Smart Citations

empirical evaluation of metrics on aspect-oriented programs

Ghareb

Allen

2019

UHD J SCI TECH

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“…We identified 115 Four papers addressed bugs & debugging. Three of these papers conducted empirical studies examining bugs in ML projects [89,188,225], whereas one paper proposed a specialized debugger for ML models [35].…”

Section: Software Testing (115 Studies)mentioning

confidence: 99%

Software Engineering for AI-Based Systems: A Survey

Martínez-Fernández,

Bogner,

Franch

et al. 2021

Preprint

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AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-and speechrecognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems.To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula. CCS Concepts: • Software and its engineering → Software creation and management; • Computing methodologies → Machine learning;

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