Analysis of Risks Assessment in Multi Software Projects Development Environment Using Classification Techniques

Alharbi, Ibraheem M.; Alyoubi, Adel A.; Altuwairiqi, Majid; Ellatif, Mahmoud Abd

doi:10.1007/978-3-030-69717-4_78

Cited by 2 publications

(5 citation statements)

References 11 publications

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“…An analytical viewpoint on risk assessment across many software projects that are being performed concurrently was offered by Alharbi et al [13]. In assessing risk levels in a multi-project situation, their research showed great accuracy, with logistic regression (LR) recording a 93% accuracy and REPTree recording a 98% accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

FEPP: Advancing Software Risk Prediction in Requirements Engineering Through Innovative Rule Extraction and Multi-Class Integration

Binsawad,

Khan

2024

IEEE Access

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The increasing complexity of software projects makes it difficult to predict risks in software requirements, which is a crucial and essential part of the Software Development Life Cycle (SDLC). The failure of a software project may occur from an inability to appropriately anticipate such risks. Because it is the first stage of any software project, risk prediction has a greater significance in software requirements. Thus, ForExPlusPlus (FEPP), a novel model for risk prediction in software requirements, is proposed in this work. Standard models such as K-nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Model Tree (LMT), Random Forest (RF), and Support Vector Machine (SVM) are used to benchmark the suggested model. The dataset from the Zenodo repository is used to train these models, and standard assessment criteria are used to evaluate the results. The accuracy analysis of the models is assessed critically using the precision, F-measure (FM), and Mathew's correlation coefficient (MCC), as well as the error rate using the Kappa Statistic (KS) and Mean Absolute Error (MAE). The suggested FEPP performs better overall, with an accuracy of 96.84%, whereas KNN performs the worst, with an accuracy of 50.99%.

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Section: Literature Reviewmentioning

confidence: 99%

FEPP: Advancing Software Risk Prediction in Requirements Engineering Through Innovative Rule Extraction and Multi-Class Integration

Binsawad,

Khan

2024

IEEE Access

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“…Alharbi et al [3] offered an analytical perspective on risk assessment in the context of numerous software projects running concurrently. This research demonstrated excellent levels of accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Increasing software project failure rates are now regarded as a pressing issue because modern software systems are now more complicated and interconnected [1,2]. These unpredictable uncertainties are known as software risks, and they result from multiple risk variables inherent to the many activities of the software development lifecycle (SDLC) [3,4]. Notably, these software risks have resulted in huge financial losses and effort wastage for software companies.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, it does not allow repeatability, as expertise is not easily exchanged across the various units working on a software project [13]. As a result, it is critical that we develop intelligent modelling methods that offer more unbiased, reproducible, and explainable decision-making assistance in risk management [1,3].…”

Section: Introductionmentioning

confidence: 99%

“…The objective of researchers is to provide sturdiness in the proper prediction of software project risk [1,3]. However, the efficacy in implementing an ML technique is contingent upon the proficiency of the selected ML technique, and the dataset utilized for its development [18,19].…”

Section: Introductionmentioning

confidence: 99%

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Software Requirement Risk Prediction Using Enhanced Fuzzy Induction Models

Mamman,

Balogun,

Basri

et al. 2023

Electronics

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The development of most modern software systems is accompanied by a significant level of uncertainty, which can be attributed to the unanticipated activities that may occur throughout the software development process. As these modern software systems become more complex and drawn out, escalating software project failure rates have become a critical concern. These unforeseeable uncertainties are known as software risks, and they emerge from many risk factors inherent to the numerous activities comprising the software development lifecycle (SDLC). Consequently, these software risks have resulted in massive revenue losses for software organizations. Hence, it is imperative to address these software risks, to curb future software system failures. The subjective risk assessment (SRM) method is regarded as a viable solution to software risk problems. However, it is inherently reliant on humans and, therefore, in certain situations, imprecise, due to its dependence on an expert’s knowledge and experience. In addition, the SRM does not allow repeatability, as expertise is not easily exchanged across the different units working on a software project. Developing intelligent modelling methods that may offer more unbiased, reproducible, and explainable decision-making assistance in risk management is crucial. Hence, this research proposes enhanced fuzzy induction models for software requirement risk prediction. Specifically, the fuzzy unordered rule induction algorithm (FURIA), and its enhanced variants based on nested subset selection dichotomies, are developed for software requirement risk prediction. The suggested fuzzy induction models are based on the use of effective rule-stretching methods for the prediction process. Additionally, the proposed FURIA method is enhanced through the introduction of nested subset selection dichotomy concepts into its prediction process. The prediction performances of the proposed models are evaluated using a benchmark dataset, and are then compared with existing machine learning (ML)-based and rule-based software risk prediction models. From the experimental results, it was observed that the FURIA performed comparably, in most cases, to the rule-based and ML-based models. However, the FURIA nested dichotomy variants were superior in performance to the conventional FURIA method, and rule-based and ML-based methods, with the least accuracy, area under the curve (AUC), and Mathew’s correlation coefficient (MCC), with values of approximately 98%.

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Analysis of Risks Assessment in Multi Software Projects Development Environment Using Classification Techniques

Cited by 2 publications

References 11 publications

FEPP: Advancing Software Risk Prediction in Requirements Engineering Through Innovative Rule Extraction and Multi-Class Integration

FEPP: Advancing Software Risk Prediction in Requirements Engineering Through Innovative Rule Extraction and Multi-Class Integration

Software Requirement Risk Prediction Using Enhanced Fuzzy Induction Models

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