Requirement-oriented risk management for incremental software development

Roy, Mandira; Deb, Novarun; Cortesi, Agostino; Chaki, Rituparna; Chaki, Nabendu

doi:10.1007/s11334-021-00406-6

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…Article (J7) proposes a risk management framework for the ISD (Incremental Software Development) process that estimates risk exposure for a project. The framework offers appropriate risk reduction strategies and works with the risk assessment module [58]. However, this proposed framework does not yet link the various planning stages with identifying risks at different project stages.…”

Section: What Is the Results Of The Research?mentioning

confidence: 99%

Risk Management in Software Development Projects: A Systematic Literature Review

Pilliang

Munawar

2022

khif

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Risk Management is an integral part of every project. Risk management must estimate the risks’ significance, especially in the SDLC process, and mitigate those risks. Since 2016, many papers and journals have researched planning, design, and risk control in software development projects over the last five years. This study aims to find the most exciting topics for researchers in risk management, especially in software engineering projects. This paper takes a systematic approach to reviewing articles containing risk management in software development projects. This study collects papers and journals included in the international online library database, then summarizes them according to the stages of the PICOC methodology. This paper results in the focus of research in the last five years on Agile methods. The current issue is that many researchers are trying to explicitly integrate risk management into the Agile development process by creating a comprehensive risk management framework. This SLR helps future research get a theoretical basis to solve the studied problem. The SLR explains the focuses of previous research, analysis of research results, and the weaknesses of the investigation. For further study, take one of the topic papers, do a critical review, and find research gaps.

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Section: What Is the Results Of The Research?mentioning

confidence: 99%

Risk Management in Software Development Projects: A Systematic Literature Review

Pilliang

Munawar

2022

khif

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“…Majority of the existing works present conflict detection frameworks that uses potential conflict catalogs or conflict rule bases 32–34 . However, the notion of NFR conflicts is very dynamic and it does not seem logical to hard code them in the form of catalogs or rule bases.…”

Section: Related Workmentioning

confidence: 99%

“…Bang et al 19 Majority of the existing works present conflict detection frameworks that uses potential conflict catalogs or conflict rule bases. [32][33][34] However, the notion of NFR conflicts is very dynamic and it does not seem logical to hard code them in the form of catalogs or rule bases. Even for the same software application, different end-users may prioritize the associated NFRs differently.…”

Section: Related Workmentioning

confidence: 99%

Minimising conflicts among run‐time non‐functional requirements within DevOps

Das,

Deb,

Chaki

et al. 2023

Systems Engineering

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Significant contributions in the existing literature highlight the potential of softgoal interdependency graphs towards analyzing conflicting non‐functional requirements (NFRs). However, such analysis is often at a very abstract level and does not quite consider the run‐time performance statistics of NFR operationalizations. On the contrary, some initial empirical evaluations demonstrate the importance of the run‐time statistics. In this paper, a framework is proposed that uses these statistics and combines the same with NFR priorities for computing the impact of NFR conflicts. The proposed framework is capable of identifying the best possible set of NFR operationalizations that minimizes the impact of conflicting NFRs. A detailed space analysis of the solution framework helps proving the efficiency of the proposed pruning mechanism in terms of better space management. Furthermore, a Dynamic Bayesian Network (DBN) ‐ based system behavioral model that works on top of the proposed framework, is defined and analyzed. An appropriate tool prototype for the framework is implemented as part of this research.

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“…That process can introduce biases and variability in risk management, leading to inconsistent risk evaluations and unpreparedness for critical issues. An inappropriate identification of risks can have a relevant significance, especially in software requirements, because misunderstood or poorly managed requirements can lead to software failing to meet its intended use, causing cost overruns, delays, or even project failure [8].…”

Section: Introductionmentioning

confidence: 99%

Comparing machine learning techniques for software requirements risk prediction

Vera,

Del Carpio

2024

IJEECS

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Software requirements are the most critical phase focused on documenting, eliciting, and maintaining the stakeholders’ requirements. Risk identification and analysis are preemptive actions designed to anticipate and prepare for potential issues. Usually, this classification of risks is done manually, a practice that the personal judgment of the risk analyst or the project manager might influence. Machine learning (ML) techniques were proposed to predict the risk level in software requirements. The techniques used were logistic regression (LR), multilayer perceptron (MLP) neural network, support vector machine (SVM), decision tree (DT), naive bayes, and random forest (RF). Each model was trained and tested using cross-validation with k-folds, each with its respective parameters, to provide optimal results. Finally, they were compared based on precision, accuracy, and recall metrics. Statistical tests were performed to determine if there were significant differences between the different ML techniques used to classify risks. The results concluded that the DT and RF are the techniques that best predict the risk level in software requirements.

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Requirement-oriented risk management for incremental software development

Cited by 8 publications

References 17 publications

Risk Management in Software Development Projects: A Systematic Literature Review

Risk Management in Software Development Projects: A Systematic Literature Review

Minimising conflicts among run‐time non‐functional requirements within DevOps

Comparing machine learning techniques for software requirements risk prediction

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