A systematic review on mining techniques for crosscutting concerns

Durelli, Rafael Serapilha; Santibanez, Daniel S. M.; Anquetil, Nicolas; Delamaro, Márcio Eduardo; Camargo, Valter Vieira de

doi:10.1145/2480362.2480567

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…This result reinforces the need for improving knowledge and technology on conceptual cohesion measurement. Conceptual cohesion metrics can take advantage of evolving concern mining techniques [7]. So, in future, we suggest the implementation of conceptual cohesion metrics in software measurement tools integrated with automated concern mining techniques.…”

Section: Resultsmentioning

confidence: 99%

An empirical study on how developers reason about module cohesion

Silva

Sant’Anna

Chávez

2014

Proceedings of the 13th International Conference on Modularity

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Several cohesion metrics have been proposed to support development and maintenance activities. The most traditional ones are the structural cohesion metrics, which rely on structural information in the source code. For instance, many of these metrics quantify cohesion based how methods and attributes are related to each other within a given module. Recently, conceptual cohesion metrics have been proposed for computing cohesion based on the responsibilities a given module realizes. Besides different flavors of cohesion, there is a lack of empirical evidence about how developers actually perceive cohesion and what kind of cohesion measurement aligns with developers' perception. In this paper we fill this gap by empirically investigating developers opinion through a web-based survey, which involved 80 participants from 9 countries with different levels of programming experience. We found that: most of the developers are familiar with cohesion; and they perceive cohesion based on class responsibilities, thus associating more with conceptual cohesion measurement. These results support the claim that conceptual cohesion seems to be more intuitive and closer to the human-oriented view of software cohesion. Moreover, the results showed that conceptual cohesion measurement captures the developers' notion of cohesion better than traditional structural cohesion measurement.

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Section: Resultsmentioning

confidence: 99%

An empirical study on how developers reason about module cohesion

Silva

Sant’Anna

Chávez

2014

Proceedings of the 13th International Conference on Modularity

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“…Some of them are focused in the code structure while others in the code behavior of a system, so it is recommended the use of combined techniques in order to achieve better results in terms of precision and recall [27]. They generally use techniques from data mining and data analysis like formal concept analysis and clustering and other ones such as program slicing, clone detection, pattern matching, natural language processing, dynamic analysis, and so on [8].…”

Section: Mining Of Crosscutting Concernsmentioning

confidence: 99%

“…Aspect mining (or concern mining) is another important research field very related to reverse engineering [6,7]. The main purpose is to identify source code elements that contribute to the implementation of crosscutting concerns (CCs) [8], such as persistence and cryptography. After the identification, the next step is the remodularization of the system using aspect-oriented programming [9].…”

Section: Introductionmentioning

confidence: 99%

A combined approach for concern identification in KDM models

2015

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Background: Systems are considered legacy when their maintenance costs raise to unmanageable levels, but they still deliver valuable benefits for companies. One intrinsic problem of this kind of system is the presence of crosscutting concerns in their architecture, hindering its comprehension and evolution. Architecture-driven modernization (ADM) is the new generation of reengineering in which models are used as main artifacts during the whole process. Using ADM, it is possible to modernize legacy systems by remodularizing their concerns in a more modular shape. In this sense, the first step is the identification of source code elements that contribute to the implementation of those concerns, a process known as concern mining. Although there exist a number of concern mining approaches in the literature, none of them are devoted to ADM, leading individual groups to create their own ad hoc proprietary solutions. In this paper, we propose an approach called crosscutting-concern knowledge discovery meta-model (CCKDM) whose goal is to mine crosscutting concerns in ADM context. Our approach employs a combination of a concern library and a K-means clustering algorithm. Methods: We have conducted an experimental study composed of two analyses. The first one aimed to identify the most suitable levenshtein values to apply the clustering algorithm. The second one aimed to check the recall and precision of our approach when compared to oracles and also to two other existing mining techniques (XScan and Timna) found in literature. Results: The main result of this work is a combined mining approach for KDM that enables a concern-oriented modernization to be performed. As a secondary and more general result, this work shows that it is possible to adapt existing concern mining code-level approaches for being used in ADM processes and maintain the same level of precision and recall. Conclusions: By using the approach herein presented, it was possible to conclude the following: (i) it is possible to automate the identification of crosscutting concerns in KDM models and (ii) the results are similar or equal to other approaches.

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“…It considers 93 studies from the last decade and it is shown that safety and timing requirements have been extensively analysed and verified, but there is a lack of work on key phases like requirements elicitation and management, while also the adoption of industrial standards is largely missing and so are methods to handle the currently critical concerns of privacy and trust requirements. Additionally, worth mentioning are some earlier studies, in particular in specific application domains [200][201][202][203][204], as well as on teaching FM [205].…”

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confidence: 99%

Empirical Formal Methods: Guidelines for Performing Empirical Studies on Formal Methods

Beek

Ferrari

2022

Software

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Empirical studies on formal methods and tools are rare. In this paper, we provide guidelines for such studies. We mention their main ingredients and then define nine different study strategies (usability testing, laboratory experiments with software and human subjects, case studies, qualitative studies, surveys, judgement studies, systematic literature reviews, and systematic mapping studies) and discuss for each of them their crucial characteristics, the difficulties of applying them to formal methods and tools, typical threats to validity, their maturity in formal methods, pointers to external guidelines, and pointers to studies in other fields. We conclude with a number of challenges for empirical formal methods.

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A systematic review on mining techniques for crosscutting concerns

Cited by 5 publications

References 30 publications

An empirical study on how developers reason about module cohesion

An empirical study on how developers reason about module cohesion

A combined approach for concern identification in KDM models

Empirical Formal Methods: Guidelines for Performing Empirical Studies on Formal Methods

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