Using Relational Topic Models to capture coupling among classes in object-oriented software systems

Gethers, Malcom; Poshyvanyk, Denys

doi:10.1109/icsm.2010.5609687

Cited by 90 publications

(71 citation statements)

References 39 publications

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“…IR-based methods have been applied to support practical tasks. For instance, IR methods have been successfully used to support feature location (Liu et al 2007;Eaddy et al 2008;Revelle and Poshyvanyk 2009;Revelle et al 2010;Dit et al 2012a;Dit et al 2012b;, traceability link recovery (Antoniol et al 2002;Hayes et al 2006;De Lucia et al 2007;Cleland-Huang et al 2010;Oliveto et al 2010;Gethers et al 2011), and impact analysis (Antoniol et al 2000;Canfora and Cerulo 2005;Poshyvanyk et al 2009;Gethers and Poshyvanyk 2010;Gethers et al 2012). We do not discuss other applications of IR-based techniques in the context of software maintenance due to space limitations; however, interested readers are referred to (Binkley and Lawrie 2010a;Binkley and Lawrie 2010b) for such an overview.…”

Section: Conceptual Information In Softwarementioning

confidence: 99%

“…Coupling measures have been also used to support impact analysis in Object Oriented systems Wilkie and Kitchenham 2000;Poshyvanyk et al 2009;Gethers and Poshyvanyk 2010). Wilkie and Kitchenham (Wilkie and Kitchenham 2000) investigated if classes with high CBO (Coupling Between Objects) coupling metric values are more likely to be affected by change ripple effects.…”

Section: Software Change Impact Analysis (Ia)mentioning

confidence: 99%

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Integrating conceptual and logical couplings for change impact analysis in software

2012

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The paper presents an approach that combines conceptual and evolutionary techniques to support change impact analysis in source code. Conceptual couplings capture the extent to which domain concepts and software artifacts are related to each other. This information is derived using Information Retrieval based analysis of textual software artifacts that are found in a single version of software (e.g., comments and identifiers in a single snapshot of source code). Evolutionary couplings capture the extent to which software artifacts were co-changed. This information is derived from analyzing patterns, relationships, and relevant information of source code changes mined from multiple versions in software repositories. The premise is that such combined methods provide improvements to the accuracy of impact sets compared to the two individual approaches.A rigorous empirical assessment on the changes of the open source systems Apache httpd, ArgoUML, iBatis, KOffice, and jEdit is also reported. The impact sets are evaluated at the file and method levels of granularity for all the software systems considered in the empirical evaluation.The results show that a combination of conceptual and evolutionary techniques, across several cutoff points and periods of history, provides statistically significant improvements in accuracy over either of the two techniques used independently. Improvements in F-measure values of up to 14%(from 3% to 17%) over the conceptual technique in ArgoUML at the method granularity, and up to 21% over the evolutionary technique in iBatis (from 9% to 30%) at the file granularity were reported.

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Section: Conceptual Information In Softwarementioning

confidence: 99%

Section: Software Change Impact Analysis (Ia)mentioning

confidence: 99%

Integrating conceptual and logical couplings for change impact analysis in software

2012

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“…In Methodbook we configured the RTM parameters as done in the work by Gethers and Poshyvanyk [38]. Our choice is due to the fact that also Gethers and Poshyvanyk [38] have applied RTM to text extracted from source code, and in particular to measure coupling between classes.…”

Section: Rtm Configuration Used In Methodbookmentioning

confidence: 99%

“…Our choice is due to the fact that also Gethers and Poshyvanyk [38] have applied RTM to text extracted from source code, and in particular to measure coupling between classes. The following setting was used:…”

Section: Rtm Configuration Used In Methodbookmentioning

confidence: 99%

“…Proposed applications of RTM include identifying potential friends within a social network of users, suggesting citations for a given scientific paper, locating web pages relevant to a web page of interest, and analyzing software artifacts to assist with software maintenance tasks and other tasks [26], [38], [39], [40], [41], [42].…”

Section: Relational Topic Modelmentioning

confidence: 99%

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Methodbook: Recommending Move Method Refactorings via Relational Topic Models

Bavota

Oliveto

Gethers

et al. 2014

IIEEE Trans. Software Eng.

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126

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Abstract-During software maintenance and evolution the internal structure of the software system undergoes continuous changes. These modifications drift the source code away from its original design, thus deteriorating its quality, including cohesion and coupling of classes. Several refactoring methods have been proposed to overcome this problem. In this paper we propose a novel technique to identify Move Method refactoring opportunities and remove the Feature Envy bad smell from source code. Our approach, coined as Methodbook, is based on Relational Topic Models (RTM), a probabilistic technique for representing and modeling topics, documents (in our case methods) and known relationships among these. Methodbook uses RTM to analyze both structural and textual information gleaned from software to better support move method refactoring. We evaluated Methodbook in two case studies. The first study has been executed on six software systems to analyze if the move method operations suggested by Methodbook help to improve the design quality of the systems as captured by quality metrics. The second study has been conducted with eighty developers that evaluated the refactoring recommendations produced by Methodbook. The achieved results indicate that Methodbook provides accurate and meaningful recommendations for move method refactoring operations.

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Predicting dependences using domain‐based coupling

Aryani

Perin

Lungu

et al. 2013

J Software Evolu Process

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Software dependences play a vital role in programme comprehension, change impact analysis and other software maintenance activities. Traditionally, these activities are supported by source code analysis; however, the source code is sometimes inaccessible or difficult to analyse, as in hybrid systems composed of source code in multiple languages using various paradigms (e.g. object-oriented programming and relational databases). Moreover, not all stakeholders have adequate knowledge to perform such analyses. For example, non-technical domain experts and consultants raise most maintenance requests; however, they cannot predict the cost and impact of the requested changes without the support of the developers. We propose a novel approach to predicting software dependences by exploiting the coupling present in domain-level information. Our approach is independent of the software implementation; hence, it can be used to approximate architectural dependences without access to the source code or the database. As such, it can be applied to hybrid systems with heterogeneous source code or legacy systems with missing source code. In addition, this approach is based solely on information visible and understandable to domain users; therefore, it can be efficiently used by domain experts without the support of software developers. We evaluate our approach with a case study on a large-scale enterprise system, in which we demonstrate how up to 65% of the source code dependences and 77% of the database dependences are predicted solely based on domain information. Definition 6Given two tables t, t 0 2 TBL, we say that t has a direct relation to t 0 if and only if t. FK. t 0 . Definition 7Given two tables t, t 0 2 TBL, we say that t has indirect relation to t 0 if and only if t. FK. FK À 1 . t 0 . 58 AMIR ARYANI ET AL.We use the well-known definitions of precision (P q ) and recall (R) to evaluate the outcomes of a given query: 60 AMIR ARYANI ET AL.• What kinds of software changes and maintenance activities can benefit the most from the proposed approach? We envisage that the main application of the proposed approach will be estimating the change propagation prior to maintenance activities such as bug fixes and software enhancements. Lientz and Swanson [24] classified software changes as perfective, adaptive, corrective and 70 AMIR ARYANI ET AL.

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Using Relational Topic Models to capture coupling among classes in object-oriented software systems

Cited by 90 publications

References 39 publications

Integrating conceptual and logical couplings for change impact analysis in software

Integrating conceptual and logical couplings for change impact analysis in software

Methodbook: Recommending Move Method Refactorings via Relational Topic Models

Predicting dependences using domain‐based coupling

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