Software engineering for AI based software products

Druffel, Larry E.; Little, Reed

doi:10.1016/0169-023x(90)90006-y

Cited by 9 publications

(5 citation statements)

References 14 publications

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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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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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show abstract

“…The curriculum and practice systems are supposed to work together to create all of these characteristics. [8] and IEEE from ACM have both been brought to China as worldwide standard information systems for curriculum design and development. Every one of these worldwide standard information systems has a structured categorization of fundamental concepts and specialised information for many specialties.…”

Section: Background Analysismentioning

confidence: 99%

“…Fig 1. Expected qualities of a typical software development employee CSDA Training Platform and Guidebook to the Software Body of Knowledge (SWEBOK) from Software Engineering Education Knowledge (SEEK)[8] and IEEE from ACM have both been brought to China as worldwide standard information systems for curriculum design and development. Every one of these worldwide standard information systems has a structured categorization of fundamental concepts and specialised information for many specialties.…”

mentioning

confidence: 99%

A Capability Maturity Model for STP aware Software Development

Sllian

Mazur

2022

JMC

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There has been an increase in the importance of software Security, Trust, and Privacy (STP). Product systems must be designed with trustworthy STP protection methods while still rendering the required benefits of applications to its consumers. As a result of this large skill gap, colleges and the software sector have found themselves in a state of supply- and-demand conflict. STP-aware software development requires a new practice Capability Maturity Model (CMM) to address this issue. In order to help colleges progressively increase their students' capacity to apply what they have learned in the classroom, this contribution provides a model that consists of 4 levels: Awareness, Curriculum, Project, and Enterprise, for STP-aware software development. Software development that is STP-aware has been shown to be quite beneficial in the development of programming talent's practice capabilities for learners.

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“…Engineering traditional software (or conventional software [136]) is about the implementation of programs (arithmetic & logic operations, a sequence of if-then-else rules, etc.) explicitly by engineers in the form of source code, which can be decomposed into units (e.g., classes, methods, functions, etc.)…”

Section: Traditional Software and Machine Learning Systemsmentioning

confidence: 99%

“…Practitioners and academicians do not use a standard term to name systems that incorporate AI/ML/DL capabilities. Software that applies AI techniques can be referred to "AI software" [137] or "AI-based software" [136]. In Cyber-Physical systems, a component whose behavior is driven by an ML/DL model obtained via training and updated through a learning process is called a "learning-enabled component" [138].…”

Section: Traditional Software and Machine Learning Systemsmentioning

confidence: 99%