A 20‐year mapping of Bayesian belief networks in software project management

Sousa, Abraham L. R. de; Souza, Cleidson R. B. de; Reis, Roberta Sena

doi:10.1049/sfw2.12043

Cited by 14 publications

(6 citation statements)

References 105 publications

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“…In this sense, in the results obtained, only some of them appeared as relevant in the clusters, such as neural networks, fuzzy logic (Measurement domain), and Bayesian networks (Uncertainty domain). The importance of this last technique in recent years in the field of project management and uncertainty has already been pointed out by other authors [50].…”

Section: Discussionmentioning

confidence: 73%

Bibliometric Analysis of the Application of Artificial Intelligence Techniques to the Management of Innovation Projects

et al. 2022

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Due to their specific characteristics, innovation projects are developed in contexts with great volatility, uncertainty, complexity, and even ambiguity. Project management has needed to adopt changes to ensure success in this type of project. Artificial intelligence (AI) techniques are being used in these changing environments to increase productivity. This work collected and analyzed those areas of technological innovation project management, such as risk management, costs, and deadlines, in which the application of artificial-intelligence techniques is having the greatest impact. With this objective, a search was carried out in the Scopus database including the three areas involved, that is, artificial intelligence, project management, and research and innovation. The resulting document set was analyzed using the co-word bibliographic method. Then, the results obtained were analyzed first from a global point of view and then specifically for each of the domains that the Project Management Institute (PMI) defines in project management. Some of the findings obtained indicate that sectors such as construction, software and product development, and systems such as knowledge management or decision-support systems have studied and applied the possibilities of artificial intelligence more intensively.

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Section: Discussionmentioning

confidence: 73%

Bibliometric Analysis of the Application of Artificial Intelligence Techniques to the Management of Innovation Projects

et al. 2022

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“…Out of the six secondary studies analyzed, four were published in journals, while the remaining two appeared in workshop proceedings. In terms of study types, we identified three systematic mapping studies [2,11,12], two systematic literature reviews (SLRs) [18,21], and one literature survey (LS) [16].…”

Section: Resultsmentioning

confidence: 99%

“…Various combinations of these techniques were also employed, such as utilizing expert knowledge and learning from data with an algorithm/heuristic, expert knowledge and formal knowledge/model, or formal knowledge/model and learning from data with an algorithm/heuristic. Rodriguez et al (S4 [18]) did not provide details on how the BN structures were constructed, while Sousa et al (S6 [2]) only mentioned the use of data and/or expert knowledge without explicitly referring to the construction of BN structures.…”

Section: Rq1: Techniques For Structure Learningmentioning

confidence: 99%

Shedding Light on the Techniques for Building Bayesian Networks in Software Engineering

Rique,

Dantas,

Albuquerque

et al. 2023

Anais Do III Workshop Brasileiro De Engenharia De Software Inteligente (ISE 2023)

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Context Bayesian networks (BNs) have been used to tackle several software engineering (SE) problems, such as risk management and effort estimation. They enable reasoning under uncertainty and have the advantage of incorporating expert knowledge to build more accurate models when sufficient historical data are not available. Software practitioners often encounter a lack of substantial evidence concerning the usability, limitations, risks, and benefits of BNs, as is the case with many other topics in the SE literature. Therefore, there is a need to organize and systematize the existing knowledge in this area. Objective This paper aims to provide researchers and practitioners with an overview of the techniques for building BNs in SE. Method We conducted a tertiary study following the guidelines available in the SE literature. Results We examined six secondary studies. Our findings revealed that expert knowledge emerges as the predominant technique for structure learning and, in conjunction with learning from data using automated tools, is widely employed for parameter learning in BNs. Conclusion Despite the attention given to data-driven approaches in SE, it is worth acknowledging the significant value that expert knowledge continues to hold in constructing more accurate and robust models. This observation underscores potential opportunities for developing expert-driven solutions to enhance model building and foster the adoption of BNs in the software industry.

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“…de Sousa et al. provide an overview of 20 years of research in BNs applied to software project management [29]. They claim that most of the studies using BNs are performed in a laboratory environment rather than in real contexts.…”

Section: Related Workmentioning

confidence: 99%

“…Indeed, this type of analysis has already been explored for other topics like anomaly detection. [29]. They claim that most of the studies using BNs are performed in a laboratory environment rather than in real contexts.…”

Section: Managing Software Quality Based On Software Logsmentioning

confidence: 99%

Bayesian Network analysis of software logs for data‐driven software maintenance

2023

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Software organisations aim to develop and maintain high-quality software systems. Due to large amounts of behaviour data available, software organisations can conduct datadriven software maintenance. Indeed, software quality assurance and improvement programs have attracted many researchers' attention. Bayesian Networks (BNs) are proposed as a log analysis technique to discover poor performance indicators in a system and to explore usage patterns that usually require temporal analysis. For this, an action research study is designed and conducted to improve the software quality and the user experience of a web application using BNs as a technique to analyse software logs. To this aim, three models with BNs are created. As a result, multiple enhancement points have been identified within the application ranging from performance issues and errors to recurring user usage patterns. These enhancement points enable the creation of cards in the Scrum process of the web application, contributing to its data-driven software maintenance. Finally, the authors consider that BNs within quality-aware and data-driven software maintenance have great potential as a software log analysis technique and encourage the community to deepen its possible applications. For this, the applied methodology and a replication package are shared. K E Y W O R D SBayes methods, software maintenance, software quality | INTRODUCTIONSoftware quality management is a key activity for the maintenance of software systems. Indeed, the budget that companies invest in software quality management is estimated to increase in the next years [1]. Software systems can generate large amounts of data both from its internal state and from how it is being used. The maintenance of such systems can benefit from the analysis of the available data to get better insights of the system, thus, performing data-driven software maintenance. For instance, companies can analyse the user experiences of their software systems [2] in order to detect possible defects, such as performance downgrades or unexpected errors. To improve the user experience, it is key to understand the users' usage patterns when navigating a software system. Understanding these patterns enables companies the ability to enhance the design of their software systems [3] as well as provide personalised information depending on the user [4]. In this regard, software logs are a popular resource to monitor.Developers place Application Programming Interface calls to a logging framework (e.g., SLF4J [5] or Log4J [6]) to record events that describe the internal state of the system as it runs in production. The analysis of software logs has been proved useful in improving system comprehension [7,8], performance analysis [9, 10], finding usage patterns [11,12], and anomaly detection [13,14]. Nevertheless, this analysis is not free from its own challenges [15].Regarding software logs analysis, the introduction of Bayesian Networks (BNs) based on behaviour data to improve performance and users' experience has not been ...

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A 20‐year mapping of Bayesian belief networks in software project management

Cited by 14 publications

References 105 publications

Bibliometric Analysis of the Application of Artificial Intelligence Techniques to the Management of Innovation Projects

Bibliometric Analysis of the Application of Artificial Intelligence Techniques to the Management of Innovation Projects

Shedding Light on the Techniques for Building Bayesian Networks in Software Engineering

Bayesian Network analysis of software logs for data‐driven software maintenance

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