Predicting Vulnerability for Requirements

Imtiaz, Sayem Mohammad; Amin, Rayhan; Quoc, Anh; Iannucci, Stefano; Bhowmik, Tanmay

doi:10.1109/iri51335.2021.00028

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…AI techniques such as DL can be used in the planning phase of SDLC to help predict which SDLC models are suitable for usage (Dhami et al 2021) and to improve function Point-Based Software Size estimation (Zhang et al 2021). Related to the analysis phase, ML techniques can help with software requirement specifications (Akshatha Nayak et al 2022;Quba et al 2021) and can also be used to predict software vulnerabilities (Imtiaz et al, 2021). Related to the design phase, various AI techniques can be used, such as ML which is used to assist in predicting software bug (Delphine Immaculate et al 2019) and automate the assumption identification process (Li et al 2019), NLP to create DFDs (Cheema et al 2023) and used for voice-driven modeling software (Black et al 2021), Artificial Neural Network (ANN) for software bug prediction (P and Kambli 2020), as well as the use of tools based on intelligence decision support systems used in risk management software (Asif and Ahmed 2020).…”

Section: The Current State Of Ai Technique Application In Sdlcmentioning

confidence: 99%

Analyst’s Perception on the Use of AI-based Tools in the Software Development Life Cycle

Giffari,

Ridho,

Indra Sensuse

et al. 2024

J. Sistem Inf. (J. Inf. Sys.)

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Artificial Intelligence (AI) integration has been the goal in many industries, including in the software development industry. One example of this integration comes in the form of integrating AI in the Software Development Lifecycle (SDLC). To date, the difficulties of incorporating AI-based tools into particular phases of SDLC have not received much attention in research. Using qualitative approach, this study aims to discover the perception on the use of AI-based tools and challenges in integrating them in the analysis phase of SDLC. The study finds out that analyst have positive perception about integrating this technology in their field of work but there are some challenges while integrating this technology such as data security and reliability concern, dependency, and adapting to this technology. This study also discovers some key factors of why some analysts adopt or refuse this technology namely time, urgency, budget, and insecurities of the users.

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