Requirement Risk Level Forecast Using Bayesian Networks Classifiers

Cano, Isabel María Del Águila

doi:10.1142/s0218194011005219

Cited by 7 publications

(2 citation statements)

References 31 publications

(45 reference statements)

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“…S18 [47] . A probabilistic model for risk assessment in security requirements S19 [14] . A BN model for identifying risky requirements based on requirements metrics S20 [35] .…”

Section: Limitationsmentioning

confidence: 99%

Bayesian networks for enhancement of requirements engineering: a literature review

Cano

2015

Requirements Eng

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Requirements analysis is the software engineering stage that is closest to the users' world. It also involves tasks that are knowledge intensive. Thus, the use of Bayesian networks (BNs) to model this knowledge would be a valuable aid. These probabilistic models could manage the imprecision and ambiguities usually present in requirements engineering (RE). In this work, we conduct a literature review focusing on where and how BNs are applied on subareas of RE in order to identify which gaps remain uncovered and which methods might engineers employ to incorporate this intelligent technique into their own requirements processes. The scarcity of identified studies (there are only 20) suggests that not all RE areas have been properly investigated in the literature. The evidence available for adopting BNs into RE is sufficiently mature yet the methods applied are not easily translatable to other topics. Nonetheless, there are enough studies supporting the applicability of synergistic cooperation between RE and BNs. This work provides a background for understanding the current state of research encompassing RE and BNs. Functional, non-functional and -ilities requirements artifacts are enhanced by the use of BNs. These models were obtained by interacting with experts or by learning from databases. The most common criticism from the point of view of BN experts is that the models lack validation, whereas requirements engineers point to the lack of a clear application method for BNs and the lack of tools for incorporating them as built-in help functions.

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“…S18 [47] . A probabilistic model for risk assessment in security requirements S19 [14] . A BN model for identifying risky requirements based on requirements metrics S20 [35] .…”

Section: Limitationsmentioning

confidence: 99%

Bayesian networks for enhancement of requirements engineering: a literature review

Cano

2015

Requirements Eng

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“…Only one article was conducted on the Bayesian network. One [105] used numerous databases to gather metrics that were taken from the design specifications for three separate NASA programs, built for the instruments for the Spacecraft, a real-time ground prediction framework and flight satellite applications. Explore the use in requirement engineering of BN, focusing specifically on identifying and evaluating the risky requirements.…”

Section: Studies Conducted On Machine Learning Methodsmentioning

confidence: 99%

Software Project Management Using Machine Learning Technique—A Review

et al. 2021

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Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects; the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction. Our contribution also offers a more comprehensive perspective and a context that would be important for potential work in project risk management. In conclusion, we have shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.

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Stability prediction of the software requirements specification

Sagrado

Cano

2017

Software Qual J

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Complex decision-making is a prominent aspect of Requirements Engineering. This work presents the Bayesian network Requisites that predicts whether the requirements specification documents have to be revised. We show how to validate Requisites by means of metrics obtained from a large complex software project. Besides, this Bayesian network has been integrated into a software tool by defining a communication interface inside a multilayer architecture to add this a new decision making functionality. It provides requirements engineers a way of exploring the software requirement specification by combining requirement metrics and the probability values estimated by the Bayesian network.

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Requirement Risk Level Forecast Using Bayesian Networks Classifiers

Cited by 7 publications

References 31 publications

Bayesian networks for enhancement of requirements engineering: a literature review

Bayesian networks for enhancement of requirements engineering: a literature review

Software Project Management Using Machine Learning Technique—A Review

Stability prediction of the software requirements specification

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