A Probabilistic Based Approach towards Software System Clustering

Corazza, Anna; Martino, Sergio Di; Scanniello, Giuseppe

doi:10.1109/csmr.2010.36

Cited by 48 publications

(59 citation statements)

References 25 publications

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“…However, a lot of important information, such as design decisions and rationale, is embedded in the comments and identifiers in source code classes. Other remodularization approaches also exploit conceptual (or semantic) information in addition to structural information [Bavota et al 2010;Maletic and Marcus 2001;Kuhn et al 2007;Scanniello et al 2010;Corazza et al 2010;Corazza et al 2011]. Bavota et al [Bavota et al 2010] used graph theory to identify extract package refactoring operations.…”

Section: Related Workmentioning

confidence: 99%

Improving software modularization via automated analysis of latent topics and dependencies

Bavota

Gethers

Oliveto

et al. 2014

ACM Trans. Softw. Eng. Methodol.

100

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Oftentimes, during software maintenance the original program modularization decays, thus reducing its quality. One of the main reasons for such architectural erosion is suboptimal placement of source code classes in software packages. To alleviate this issue, we propose an automated approach to help developers improve the quality of software modularization. Our approach analyzes underlying latent topics in source code as well as structural dependencies to recommend (and explain) refactoring operations aiming at moving a class to a more suitable package. The topics are acquired via Relational Topic Models (RTM), a probabilistic topic modeling technique. The resulting tool, coined as R3 (Rational Refactoring via RTM), has been evaluated in two empirical studies. The results of the first study conducted on nine software systems indicate that R3 provides a coupling reduction from 10% to 30% among the software modules. The second study with 62 developers confirms that R3 is able to provide meaningful recommendations (and explanations) for move class refactoring. Specifically, more than 70% of the recommendations were considered meaningful from a functional point of view.

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Section: Related Workmentioning

confidence: 99%

Improving software modularization via automated analysis of latent topics and dependencies

Bavota

Gethers

Oliveto

et al. 2014

ACM Trans. Softw. Eng. Methodol.

100

View full text Add to dashboard Cite

show abstract

“…• Algorithm for Comprehension-Driven Clustering (ACDC) [47] [14], [15] and two of its variants, • Architecture Recovery using Concerns (ARC) [21]. The selected techniques have been previously published, they are automated, and they have been designed specifically for architecture recovery.…”

Section: Selected Recovery Techniquesmentioning

confidence: 99%

“…Recent work has focused on utilizing textual input [14], [15], [33], [21] obtained from source code and comments. The resulting techniques combine textual input with other commonly-used architecture recovery mechanisms, such as objective function maximization or hierarchical clustering.…”

Section: Related Workmentioning

confidence: 99%

“…For our comparative analysis, we have considered both variants of Bunch-NAHC and SAHC-to obtain the most accurate clustering that this technique can provide. ZBR, or zone-based recovery, is a technique developed by Corrazza et al [14], [15], which utilizes textual information, hierarchical clustering, and a weighting scheme for feature vectors. We have selected the latest formulation of Corazza et al's technique [15].…”

Section: Selected Recovery Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

A comparative analysis of software architecture recovery techniques

Garcia

Ivkovic

Medvidović

2013

2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE)

124

141

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Abstract-Many automated techniques of varying accuracy have been developed to help recover the architecture of a software system from its implementation. However, rigorously assessing these techniques has been hampered by the lack of architectural "ground truths". Over the past several years, we have collected a set of eight architectures that have been recovered from opensource systems and independently, carefully verified. In this paper, we use these architectures as ground truths in performing a comparative analysis of six state-of-the-art software architecture recovery techniques. We use a number of metrics to assess each technique for its ability to identify a system's architectural components and overall architectural structure. Our results suggest that two of the techniques routinely outperform the rest, but even the best of the lot has surprisingly low accuracy. Based on the empirical data, we identify several avenues of future research in software architecture recovery.

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“…Six techniques are selected for this purpose which are: ACDC [6], BUNCH [7], WCA [18], LIMBO [19], ZoneBased Recovery (ZBR) [31] and Architecture Recovery using Concerns (ARC) [32]. These techniques are evaluated in terms of accuracy, authoritativeness and in terms of the recovery criteria used.…”

Section: Related Workmentioning

confidence: 99%

Supervised Learning For Orphan Adoption Problem In Software Architecture Recovery

Bibi

Maqbool

Kanwal

2016

MJCS

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Maintenance of architectural documentation is a prime requirement for evolving software systems. New versions of software systems are launched after making the changes that take place in a software system over time. The orphan adoption problem, which deals with the issue of accommodation of newly introduced resources (orphan resources) in appropriate subsystems in successive versions of a software system, is a significant problem. The orphan adoption algorithm has been developed to address this problem. For evolving software systems, it would be useful to recover the architecture of subsequent versions of a software system by using existing architectural information. In this paper, we explore supervised learning techniques (classifiers) for recovering the architecture of subsequent versions of a software system by taking benefit of existing architectural information. We use three classifiers, i.e., Bayesian classifier, k-Nearest Neighbor classifier and Neural Network for orphan adoption. We conduct experiments to compare the performance of the classifiers using various dependencies between entities in a software system. Our experiments highlight correspondence between the orphan adoption algorithm and the classifiers, and also reveal their strengths and weaknesses. To combine strengths of individual classifiers, we propose using a multiclassifier approach in which classifiers work cooperatively to improve classification accuracy. Experiments show that there is significant improvement in results when our proposed multiclassifier approach is used.

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A Probabilistic Based Approach towards Software System Clustering

Cited by 48 publications

References 25 publications

Improving software modularization via automated analysis of latent topics and dependencies

Improving software modularization via automated analysis of latent topics and dependencies

A comparative analysis of software architecture recovery techniques

Supervised Learning For Orphan Adoption Problem In Software Architecture Recovery

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