Visualization and analysis of software engineering data using self-organizing maps

MacDonell, Stephen G.

doi:10.1109/isese.2005.1541820

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…In terms of visualization and analysis of software engineering data, the research conducted by MacDonell [14] presents a range of situations in which one would benefit from SOM. One application involves clustering software artefacts in groups with low, medium and high defect counts.…”

Section: Related Workmentioning

confidence: 99%

Detecting the Most Important Classes from Software Systems with Self Organizing Maps

Manole

2021

Studia UBB Informatica

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Self Organizing Maps (SOM) are unsupervised neural networks suited for visualisation purposes and clustering analysis. This study uses SOM to solve a software engineering problem: detecting the most important (key) classes from software projects. Key classes are meant to link the most valuable concepts of a software system and in general these are found in the solution documentation. UML models created in the design phase become deprecated in time and tend to be a source of confusion for large legacy software. Therefore, developers try to reconstruct class diagrams from the source code using reverse engineering. However, the resulting diagram is often very cluttered and difficult to understand. There is an interest for automatic tools for building concise class diagrams, but the machine learning possibilities are not fully explored at the moment. This paper proposes two possible algorithms to transform SOM in a classification algorithm to solve this task, which involves separating the important classes - that should be on the diagrams - from the others, less important ones. Moreover, SOM is a reliable visualization tool which able to provide an insight about the structure of the analysed projects.

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Section: Related Workmentioning

confidence: 99%

Detecting the Most Important Classes from Software Systems with Self Organizing Maps

Manole

2021

Studia UBB Informatica

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“…Balsera et al [55] have exposed the application of SOM to analyze information related to effort estimation and software projects features. MacDonell [56] has reported other multidimensional data study visualization based on SOM, identifying groups of data for similar projects and finding nonlinear relationships within the explored variables. Naiem et al [57] have used SOM for visualizing the…”

Section: Self-organizing Maps and Applications In Project Managementmentioning

confidence: 99%

Exploring Project Complexity through Project Failure Factors: Analysis of Cluster Patterns Using Self‐Organizing Maps

et al. 2018

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In the field of project management, complexity is closely related to project outcomes and hence project success and failure factors. Subjectivity is inherent to these concepts, which are also influenced by sectorial, cultural, and geographical differences. While theoretical frameworks to identify organizational complexity factors do exist, a thorough and multidimensional account of organizational complexity must take into account the behavior and interrelatedness of these factors. Our study is focused on analyzing the combinations of failure factors by means of self-organizing maps (SOM) and clustering techniques, thus getting different patterns about the project managers perception on influencing project failure causes and hence project complexity. The analysis is based on a survey conducted among project manager practitioners from all over the world to gather information on the degree of influence of different factors on the projects failure causes. The study is cross-sectorial. Behavioral patterns were found, concluding that in the sampled population there are five clearly differentiated groups (clusters) and at least three clear patterns of answers. The prevalent order of influence is project factors, organization related factors, project manager and team members factors, and external factors.

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“…Another preliminary study which can be done is the multidimensional data visualization for which they have used self-organizing maps SOM (MacDonell, 2005). This visualization method allows us to identify groups of data for similar projects and to find nonlinear relationships within the variables set in exploration.…”

Section: Data Understandingmentioning

confidence: 99%

Data Mining Applied to the Improvement of Project Management

Balsera¹,

Montequín²,

Fernández³

et al. 2012

Data Mining Applications in Engineering and Medicine

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Visualization and analysis of software engineering data using self-organizing maps

Abstract: There is no question that accuracy is an important requirement of classification and prediction models used

Cited by 5 publications

References 27 publications

Detecting the Most Important Classes from Software Systems with Self Organizing Maps

Detecting the Most Important Classes from Software Systems with Self Organizing Maps

Exploring Project Complexity through Project Failure Factors: Analysis of Cluster Patterns Using Self‐Organizing Maps

Data Mining Applied to the Improvement of Project Management

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