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AI-Augmented Software Engineering: Revolutionizing or Challenging Software Quality and Testing?
  • Tafline Ramos,
  • Amanda Dean,
  • David McGregor
Tafline Ramos
La Trobe University

Corresponding Author:[email protected]

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Amanda Dean
Planit
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David McGregor
Planit
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Abstract

With organizations seeking faster, cheaper, and smarter ways of delivering higher quality software, many are looking towards generative artificial intelligence (AI) to drive efficiencies and innovation throughout the software development lifecycle. However, generative AI can suffer from several fundamental issues, including a lack of traceability in concept generation and decision making, the potential for making incorrect inferences (hallucinations), shortcomings in response quality, and bias. Quality engineering (QE) has long been utilized to enable more efficient and effective delivery of higher quality software. A core aspect of QE is adopting quality models to support various lifecycle practices, including requirements definition, quality risk assessments and testing. In this position paper, we introduce the application of QE to AI systems, consider shortcomings in existing AI quality models from the International Organization for Standardization (ISO), and propose extensions to ISO models based on the results of a survey. We also reflect on skills that IT graduates may need in the future, to support delivery of better-quality AI.
18 Jul 2024Submitted to Journal of Software: Evolution and Process
18 Jul 2024Submission Checks Completed
18 Jul 2024Assigned to Editor
18 Jul 2024Reviewer(s) Assigned
18 Jul 2024Review(s) Completed, Editorial Evaluation Pending
18 Jul 2024Editorial Decision: Revise Minor
08 Oct 20241st Revision Received
10 Oct 2024Submission Checks Completed
10 Oct 2024Assigned to Editor
18 Oct 2024Review(s) Completed, Editorial Evaluation Pending
18 Oct 2024Editorial Decision: Accept