Software Clustering: Unifying Syntactic and Semantic Features

Misra, Janardan; Annervaz, K. M.; Kaulgud, Vikrant; Sengupta, Shamik; Titus, Gary

doi:10.1109/wcre.2012.21

Cited by 27 publications

(9 citation statements)

References 25 publications

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“…While a majority of the existing recovery techniques rely on structural input to identify components [18], over time researchers have also attempted to include non-structural information, such as directory paths and file authorship [7], [8], [33], [21]. Recent work has focused on utilizing textual input [14], [15], [33], [21] obtained from source code and comments.…”

Section: Related Workmentioning

confidence: 99%

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A comparative analysis of software architecture recovery techniques

Garcia

Ivkovic

Medvidović

2013

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

131

142

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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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Section: Related Workmentioning

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%

A comparative analysis of software architecture recovery techniques

Garcia

Ivkovic

Medvidović

2013

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

131

142

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“…In [11], the authors use an approach of detecting design patterns in the source code by using code smells and a set of pattern specifications. In [12], the authors give their approach to detect similarities in code. They present a highly configurable clustering workbench that allows the user to collect various source code features and then to select the code features used for clustering.They also present a comparison of outputs based on different combinations of code-features and algorithms.…”

Section: Importance Of Performance Antipatterns In Context Of Thementioning

confidence: 99%

“…Note that we cannot assume to be able to perform a dynamic analysis on applications as they are yet to be migrated to a Cloud platform. The approach leverages parts of the work in [12] to abstract out code into a set of interacting clusters of classes. Subsequently we utilize user inputs on how these interacting clusters would be distributed across various nodes in the Cloud, and employ a threshold based analysis on detecting patterns of interaction in parts of the system that could potentially exhibit performance antipatterns.…”

Section: Importance Of Performance Antipatterns In Context Of Thementioning

confidence: 99%

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Performance Antipatterns: Detection and Evaluation of Their Effects in the Cloud

Sharma¹,

Anwer

2014

2014 IEEE International Conference on Services Computing

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The way an application is designed and certain patterns thereof, play a significant role and might have a positive or a negative effect on the performance of the application. Some design patterns that have a negative effect on performance, also called performance antipatterns, may become important when evaluating migrating the application to the Cloud. Although there has been work done in the past related to defining performance antipatterns, there has been none that highlights the importance and effects of these performance antipatterns when an application is migrated to Cloud. In this work we present an approach to automatically detect important performance antipatterns in an application, by leveraging static code analysis and information about prospective deployment of the application components on the Cloud. We also experimentally show that these antipatterns may become prominent and pull down the application's performance if the application is migrated to the Cloud. Our results show that the performance of the parts of the application with such antipatterns suffer significantly and hence, the detection of these antipatterns has an overarching significance in the domain of software development for the Cloud. The approach we present here has also been implemented in a prototype cloud migration assessment tool.

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