Architectural design recovery using data mining techniques

Sartipi, Kamran; Kontogiannis, Kostas; Mavaddat, Farhad

doi:10.1109/csmr.2000.827321

Cited by 42 publications

(21 citation statements)

References 24 publications

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“…They consider source code fragments that contain many attributes involved in such rules as a collection of structurally related fragments. Sartipi et al use both association rule mining and clustering to identify structurally related fragments in the architecture recovery process [24]. These three approaches differ in both intent and form.…”

Section: Other Data Mining Approaches For Software Engineering Tasksmentioning

confidence: 99%

Predicting source code changes by mining change history

Ying

Murphy

et al. 2004

IIEEE Trans. Software Eng.

453

314

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Abstract-Software developers are often faced with modification tasks that involve source which is spread across a code base. Some dependencies between source code, such as those between source code written in different languages, are difficult to determine using existing static and dynamic analyses. To augment existing analyses and to help developers identify relevant source code during a modification task, we have developed an approach that applies data mining techniques to determine change patterns-sets of files that were changed together frequently in the past-from the change history of the code base. Our hypothesis is that the change patterns can be used to recommend potentially relevant source code to a developer performing a modification task. We show that this approach can reveal valuable dependencies by applying the approach to the Eclipse and Mozilla open source projects and by evaluating the predictability and interestingness of the recommendations produced for actual modification tasks on these systems.

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Section: Other Data Mining Approaches For Software Engineering Tasksmentioning

confidence: 99%

Predicting source code changes by mining change history

Ying

Murphy

et al. 2004

IIEEE Trans. Software Eng.

453

314

View full text Add to dashboard Cite

show abstract

“…The techniques using formal concept analysis differ in the definition of the so called formal context that defines the objects, attributes, and relation used as input to concept analysis and also in the use of the resulting lattice. Data mining techniques (de Oca and Carver 1998;Sartipi et al 2000a;Sartipi 2001) and spectral analysis (Shokoufandeh et al 2002) are also used.…”

Section: Automated Clusteringmentioning

confidence: 99%

Extending the reflexion method for consolidating software variants into product lines

et al. 2009

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Software variants emerge from ad-hoc copying in-the-large with adaptations to a specific context. As the number of variants increases, maintaining such software variants becomes more and more difficult and expensive. In contrast to such ad-hoc reuse, software product lines offer organized ways of reuse, taking advantage of similarities of different products. To re-gain control, software variants may be consolidated as organized software product lines. In this paper, we describe a method and supporting tools to compare software variants at the architectural level extending the reflexion method to software variants. Murphy's reflexion method allows one to reconstruct the module view, a static architectural view describing the static components, their interfaces and dependencies and their grouping as layers and subsystems. The method consists of the specification of the module view and the mapping of implementation components onto the module view. An automatic analysis determines differences between the module view and its implementation. We extend the reflexion method from single systems to software variants. Because software variants share a very large amount of code, we can expect components of one variant to re-occur in the other variant either identically or at least similarly. We use similarity metrics to determine this type of correspondence among implementation components between two variants. Because the similarity metrics are expensive to compute, we use clone detection techniques to reduce the number of candidate pairs of implementation components for which the similarity is computed. The correspondence is then used to transfer as much as possible of the mapping for the analyzed variants to the next variant to be analyzed. Furthermore, we describe how to unify the individual product architectures into a software product line architecture.

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“…Recovering modules and subsystems from existing software systems has proven useful in a number of ways and many methods to automatically or semiautomatically detect components have been published in the literature [2,3,[5][6][7]10,8,9,14,12,13,16,18,19,24,23,22,20,[25][26][27][28][29][32][33][34][35][37][38][39][40][42][43][44][45][46][47]49,[56][57][58][59][60]64,65,67,63,61,48,54,55,…”

Section: Introductionmentioning

confidence: 99%

Revisiting the ΔIC approach to component recovery

Czeranski

Koschke

2006

Science of Computer Programming

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Component recovery supports program understanding, architecture recovery, and re-use. Among the best known techniques for detection of re-usable objects (related global variables and their accessor functions) is IC (the improvement in internal connectivity). This paper re-visits the original approach and extends it in different ways. It describes a variant of IC suitable for reverse engineering that omits the slicing step of the original approach. The underlying metric of IC is extended toward types integrating ideas of the Internal Access technique such that abstract data types can also be detected. Furthermore, the connectivity metric of IC is combined with a cohesion metric based on vertex connectivity. The new metrics and the new algorithm for reverse engineering are evaluated and compared to other techniques quantitatively.The new contributions of this paper over the conference paper are the analysis of the relation of the connectivity threshold and recall and precision, additional experiments in varying the ICVC (internal connectivity vertex connectivity) factor, and a detailed comparison of the complementarity of the various techniques.

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Architectural design recovery using data mining techniques

Cited by 42 publications

References 24 publications

Predicting source code changes by mining change history

Predicting source code changes by mining change history

Extending the reflexion method for consolidating software variants into product lines

Revisiting the ΔIC approach to component recovery

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