Identification of data cohesive subsystems using data mining techniques

de, Carlos Montes; Carver, Doris L.

doi:10.1109/icsm.1998.738485

Cited by 33 publications

(11 citation statements)

References 18 publications

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“…These techniques include concept analysis [22,39], association rule mining [29], and clustering [42,4,10,32,1,27]. Mancoridis [23] treated clustering as an optimization problem.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic component identification

Sajnani

Lopes

2014

Proceedings of the 7th India Software Engineering Conference

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Automatic component identification is an important step for many software engineering tasks and tools, from reverse engineering to program comprehension. Although being a topic of research for a long time, generally applicable solutions remain elusive with very low recall. Most component identification techniques impose structure on the software, therefore requiring substantial knowledge about the software, instead of inferring such structure.We propose PCI, a non-parametric, probabilistic algorithm to the problem of automatic component identification. The proposed approach uses structural, behavioural and lexical features of the software to infer the underlying structure and identify the components. In order to evaluate this approach, multiple experiments are performed on a software system for which components have previously been identified [by others]. The proposed algorithm is compared to two existing algorithms. The experiments measure precision and recall, and attempt to unveil the impact of each source of information on the accuracy of the approach. We find PCI outperforms the others most notably on recall, with improvements that go from 4x to 10x better recall.

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“…These techniques include concept analysis [22,39], association rule mining [29], and clustering [42,4,10,32,1,27]. Mancoridis [23] treated clustering as an optimization problem.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic component identification

Sajnani

Lopes

2014

Proceedings of the 7th India Software Engineering Conference

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“…Based on the underlying mechanisms used to measure the cohesion of a class one can distinguish: structural metrics [46,9,13,26,27,48], the most popular class of cohesion metrics, semantic or conceptual metrics [16,19,20,33,37], information entropy-based metrics [5,25], slice-based metrics [40], metrics based on data mining [42], and metrics for specific types of applications like knowledge-based [30], aspect-oriented systems [47] and distributed systems [15]. The class of structural metrics is the most investigated category of cohesion metrics and includes: LCOM1 (lack of cohesion of methods) and LCOM2 [13], LCOM3 [27], LCOM4 and Co (connectivity) [27], LCOM5 [26], Coh [9], TCC (tight class cohesion) and LCC (loose class cohesion) [46], ICH (information-flow-based cohesion) [31].…”

Section: Related Workmentioning

confidence: 99%

Lack of Conceptual Cohesion of Methods

Jain

Gupta

2015

Proceedings of the 8th India Software Engineering Conference

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While often defined in informal ways, class cohesion reflects important properties of modules in a software system. High cohesion for classes is one of the desirable properties in Object Oriented (OO) analysis as it supports program comprehension, testing, reusability, maintainability. Cohesion metrics have been used for quality assessment, fault prediction, software modularization etc. Existing approaches of class cohesion metrics are largely based on the structural information of the source code, such as attribute references in class methods. These cohesion metrics reflect particular interpretations of cohesion. However, only looking at structural aspect of cohesion is not sufficient for completely and accurately specifying class cohesion. So there is a need to pay attention on other aspects of cohesion like conceptual aspect. But only few conceptual metrics have been proposed till now. In our work, we have proposed a new set of cohesion metrics named LCCM (Lack of Conceptual Cohesion of Methods) metrics. These cohesion metrics are conceptual version of widely used LCOM (Lack of Cohesion of methods) metrics. LCOM metrics measure cohesion on structural information extracted entirely from the source code (e.g., attribute references in methods and method calls) that captures the degree to which the elements of a class belong together from a structural point of view. Proposed LCCM metrics use conceptual concerns embedded in source code entities for measuring class cohesion. These metrics are based on the analysis of latent topics embedded in comments and identifiers in source code. Latent Drichlet Allocation (LDA), a topic modeling tool is used for this purpose. These topics are used by proposed LCCM metrics to define similarity between methods of a class and on the basis of this similarity; proposed LCCM metrics define cohesion of the class. For the verification of proposed metrics, a case study on an open source java software system, called Rhino, is performed. The case study indicates that the novel cohesion metrics capture different aspects of class cohesion compared to the exiting cohesion metrics.

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“…This burden can be caused by an increase in complexity brought on by the normal evolution of the system [75,9,58,24,25,53]. Classic symptoms include:…”

Section: Introductionmentioning

confidence: 99%

“…When applying these countermeasures in a reliable, economically sound and swift fashion, the software engineer would ideally like to have (1) a deep insight into the application in order to start his/her re-engineering operation [74,24,52] and (2) a well-covering (set of) regression test(s) to check whether the adaptations made are behavior-preserving [25,27]. In practice, legacy applications seldom have up to date documentation [58], nor do they have such a set of tests.…”

Section: Introductionmentioning

confidence: 99%