Use of FURIA for Improving Task Mining

Průcha, Petr; Skrbek, Jan

doi:10.18267/j.aip.183

Cited by 4 publications

(2 citation statements)

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“…Software risks are described by the US Department of Defense (DoD) as indices that gauge the incapacity to accomplish predetermined goals because of financial, schedule, or technological limitations [6]. Boehm established software risk management in 1989, setting the stage for further research [7].…”

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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“…Fuzzy rules are more generic than conventional rules, as they create dynamic boundaries for classification processes. For instance, the conventional rules work with fixed decision boundaries, with abrupt transitions between different classes, thereby generating questionable and non-intuitive models [21,22]. The understandability of the FURIA is, thus, a major advantage over the alternative (black box) conventional rule and ML-based methods, and a major reason for its adoption in this research.…”

Section: Introductionmentioning

confidence: 99%

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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