Does System Based on Artificial Intelligence Need Software Engineering Method? Systematic Review

Syahputri, Irdina Wanda; Ferdiana, Ridi; Kusumawardani, Sri Suning

doi:10.1109/icic50835.2020.9288582

Cited by 2 publications

References 38 publications

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Estimation Techniques for Scrum: A Qualitative Systematic Study

Jorge-Martinez

Misra

Butt

et al. 2022

Lecture Notes in Networks and Systems

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Every competitive IT industry cannot avoid underestimating their projects' effort, cost, and time. Some scrum project is completed delayed and undergoes difficulties due to over budgeting and a lack of needed functions. Software project failures are caused by incorrect and imprecise estimation; thus, it should be taken into account.A substantial change is required when Agile-based processes (e.g., Scrum) are introduced to the industry. The analysis is still difficult with Agile since requirements are constantly changing. Projects, individuals, and resistance issues, incorrect usage of cost factors, unawareness of regression testing work, readability of software requirements size as well as its related complexities, and so forth are all causes behind the difference in anticipated and real effort. This work analysis examined several publications and prospective researchers striving to narrow the actual and estimated effort gap. Decision-Based techniques significantly outperformed non-Decision Based and conventional estimating strategies by extensive literature analysis. We found that the regression test based estimation technique should be improved for accurate estimation of effort. However, scrum still needs a significant estimation technique to resolve the over budgeting issue. This study discussed the machine learning techniques, there proficiencies for estimation and flaws. The overall effort is the sum of all sprints components' efforts, and it repeats after the prospective deliverable version.

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Estimation Techniques for Scrum: A Qualitative Systematic Study

Jorge-Martinez

Misra

Butt

et al. 2022

Lecture Notes in Networks and Systems

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Artificial Intelligence Theories: Application to CommonKAD Methodology

Mambo

2024

ACRT

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Theories are required for artificial intelligence (AI) to make greater progress. Despite the development of several AI theories, their use is minimal and their nature is not widely known. An analogy with software engineering theories was used to analyze kernel, genetic, design decision, task, and AI innovation theories to determine their nature and characteristics. These theories were then applied to the CommonKAD methodology in AI to explore how they could improve the methodology, potentially contributing to the evolution of AI theories and increasing their application.

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Does System Based on Artificial Intelligence Need Software Engineering Method? Systematic Review

Cited by 2 publications

References 38 publications

Estimation Techniques for Scrum: A Qualitative Systematic Study

Estimation Techniques for Scrum: A Qualitative Systematic Study

Artificial Intelligence Theories: Application to CommonKAD Methodology

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