Pitfalls in Aspect Mining

Mens, Kim; Kellens, Andy; Krinke, Jens

doi:10.1109/wcre.2008.10

Cited by 20 publications

(17 citation statements)

References 27 publications

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“…Those itemsets are cross-cutting concern candidatesand should be checked to see if they actually represent crosscutting concerns. A short literature survey that we performed revealed that evaluation of aspect mining techniques is a weak point of many studies; a conclusion shared by Mens et al [22]. Furthermore, many evaluations are of a subjective nature and there is a lack of quantitative information, which makes comparing techniques difficult.…”

Section: Introductionmentioning

confidence: 89%

Identifying cross-cutting concerns using software repository mining

Mulder

Zaidman

2010

Proceedings of the Joint ERCIM Workshop on Software Evolution (EVOL) and International Workshop on Principles of Software Evolu

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Cross-cutting concerns are pieces of functionality that have not been captured into a separate module, thereby hindering program comprehension and maintainability. Solving these problems requires first identifying these cross-cutting concerns in pieces of software. Several methods for identification have been proposed but the option of using software repository mining has largely been left unexplored. That technique can uncover relationships between modules that may not be present in the source code and thereby provide a different perspective on the cross-cutting concerns in a software system. We perform software repository mining on the repositories of two software systems for which the crosscutting concerns are known: JHotDraw and Tomcat. Based on the results of the evaluation, we make some suggestions for future directions in the area of identifying crosscutting concerns using software repository mining ★ .

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Section: Introductionmentioning

confidence: 89%

Identifying cross-cutting concerns using software repository mining

Mulder

Zaidman

2010

Proceedings of the Joint ERCIM Workshop on Software Evolution (EVOL) and International Workshop on Principles of Software Evolu

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“…But millions of lines of code written in procedural languages, especially in COBOL, exist even today and is maintained. Second limitation is poor precision [25]. Many current-day aspect mining techniques exhibit poor precision, meaning that the percentage of relevant candidate aspects in the set of all candidates reported by a given technique is relatively low.…”

Section: Limitations Of Aspect Mining Techniquesmentioning

confidence: 99%

“…Especially for techniques that return a large number of results, this lack of precision can be problematic, since it may require significant amount of user involvement to separate the false positives from the relevant candidate aspects. Third limitation is scalability [25]. An important property of any given aspect mining technique is its scalability.…”

Section: Limitations Of Aspect Mining Techniquesmentioning

confidence: 99%

“…Although the problem of user involvement holds for several known aspect mining techniques, identifier analysis is a technique that suffers in particular from this scalability issue. Fourth limitation is lack of empirical validation of technique [25]. A good empirical validation of aspect mining techniques requires the ability to measure the precision and recall of the results, at different levels of granularity.…”

Section: Limitations Of Aspect Mining Techniquesmentioning

confidence: 99%

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A Survey of Aspect Mining Tools and Techniques

Shaikh¹,

Tanwani²

2017

IJARCSSE

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Abstract-Aspect mining is a specialized reverse engineering process to identify crosscutting concerns from legacy code. Research in aspect mining is concerned with the development of concepts, principles, methods and tools to identify cross-cutting concerns from existing software systems. A comprehensive literature survey on aspect mining tools and techniques is presented in this paper. The paper contains the current state of the art about aspect mining techniques. It reports main aspect mining techniques that have been proposed in literature and are used in various aspect mining tools. A brief description of aspect mining tools is also included.

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“…We investigate their hypothesis in the following and mine TinyOS for CCCs. Note that there are various approaches to search for CCCs [16], e. g., fan-in analysis [15]. We focus on file-level mining.…”

Section: Cross-cutting Concernsmentioning

confidence: 99%

On mining sensor network software repositories

Loukas

Woehrle

Langendoen

2011

Proceedings of the 2nd Workshop on Software Engineering for Sensor Network Applications

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Wireless Sensor Network (WSN) software is typically developed in one of the two prominent WSN operating systems: TinyOS or Contiki. Both of these operating systems are open-source projects and basically frameworks for WSN developers. In this paper, we study the software repositories of these two projects. Software repositories provide a wealth of information on software projects and their development. Based on the mined information, we explore the TinyOS and Contiki commit history and compare them to an open-source embedded operating system, Ethernut. As a second step, we explore WSN-specific artifacts and mine TinyOS software for cross-cutting concerns. Most of the relations we find are not cross-cutting. Nevertheless, we do find cross-cutting concerns that are resource-related.

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Pitfalls in Aspect Mining

Cited by 20 publications

References 27 publications

Identifying cross-cutting concerns using software repository mining

Identifying cross-cutting concerns using software repository mining

A Survey of Aspect Mining Tools and Techniques

On mining sensor network software repositories

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