Risk Management in Software Engineering: What Still Needs to Be Done

Hussain, Tajammal

doi:10.1007/978-3-030-01177-2_37

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…This article presents a framework that categorizes risks based on their relative importance and perceived level of control over the project manager. The framework is classified into four quadrants: customer mandate, scope and requirements; execution; and the environment [52]. However, risk analysis becomes very difficult or impractical for large projects because the research only focuses on quantitative aspects and ignores qualitative elements.…”

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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“…In [8], Boehm split risk assessment into three phasesidentification, analysis, and prioritization -and risk control into three other phases: management planning, resolution (or mitigation), and monitoring. Other researchers may change the categories to which these steps belong to, such as in [14], where risk assessment is made up of phases for identification, analysis, prioritization, planning, and resolution, and risk control is made up of only one phase, which is monitoring, but the key concepts and phases remain the same.…”

Section: Impactmentioning

confidence: 99%

An Analysis of the State of the Art of Machine Learning for Risk Assessment in Software Projects (S)

Sousa¹

2021

International Conferences on Software Engineering and Knowledge Engineering

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Risk management is one of the ten knowledge areas discussed in the Project Management Body of Knowledge (PMBOK), which serves as a guide that should be followed to increase the chances of project success. The popularity of research regarding the application of risk management in software projects has been consistently growing in recent years, particularly with the application of machine learning techniques to help identify risk levels or risk factors of a project before the project development begins, with the intent of improving the likelihood of success of software projects.This paper provides an overview of various concepts related to risk and risk management in software projects, including traditional techniques used to identify and control risks in software projects, as well as machine learning techniques and methods which have been applied to provide better estimates and classification of the risk levels and risk factors that can be encountered during the development of a software project. The paper also presents an analysis of machine learning oriented risk management studies and experiments found in the literature as a way of identifying the type of inputs and outputs, as well as frequent algorithms used in this research area.

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“…Risk identification and analysis are preemptive actions designed to anticipate and prepare for potential issues. It implies identifying potential risks, evaluating their likely impact and frequency, and formulating strategies for mitigation or contingency plans to tackle them proactively [5]. Risks can be categorized using different criteria, including their origin, type, or impact on the project [6].…”

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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Risk Management in Software Engineering: What Still Needs to Be Done

Cited by 4 publications

References 8 publications

Risk Management in Software Development Projects: A Systematic Literature Review

Risk Management in Software Development Projects: A Systematic Literature Review

An Analysis of the State of the Art of Machine Learning for Risk Assessment in Software Projects (S)

Comparing machine learning techniques for software requirements risk prediction

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