A Survey of Automated Code-Level Aspect Mining Techniques

Kellens, Andy; Mens, Kim; Tonella, Paolo

doi:10.1007/978-3-540-77042-8_6

Cited by 50 publications

(30 citation statements)

References 19 publications

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“…Work on aspect mining searches crosscutting concerns in the source code and aims to extract them into separate aspects [38]. In the location phase, aspect mining often focuses more on finding repeating structures in the source code (homogeneous crosscutting, clone detection), though it also employs concernlocation techniques (see below).…”

Section: Related Workmentioning

confidence: 99%

Variability Mining: Consistent Semi-automatic Detection of Product-Line Features

Kästner

Dreiling

Ostermann

2014

IIEEE Trans. Software Eng.

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Abstract-Software product line engineering is an efficient means to generate a set of tailored software products from a common implementation. However, adopting a product-line approach poses a major challenge and significant risks, since typically legacy code must be migrated toward a product line. Our aim is to lower the adoption barrier by providing semiautomatic tool support-called variability mining-to support developers in locating, documenting, and extracting implementations of product-line features from legacy code. Variability mining combines prior work on concern location, reverse engineering, and variability-aware type systems, but is tailored specifically for the use in product lines. Our work pursues three technical goals: (1) we provide a consistency indicator based on a variability-aware type system, (2) we mine features at a fine level of granularity, and (3) we exploit domain knowledge about the relationship between features when available. With a quantitative study, we demonstrate that variability mining can efficiently support developers in locating features.

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Section: Related Workmentioning

confidence: 99%

Variability Mining: Consistent Semi-automatic Detection of Product-Line Features

Kästner

Dreiling

Ostermann

2014

IIEEE Trans. Software Eng.

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“…The assumption is that developers have already identified the concerns presenting a crosscutting behavior, possibly with the support of an aspect mining tool [9,14]. We start presenting an algorithm that can be used to decide whether OO transformation are needed.…”

Section: Basic Guidelinesmentioning

confidence: 99%

Guidelines for Enabling the Extraction of Aspects from Existing Object-Oriented Code.

Valente¹,

Malta²,

Domingues³

2009

JOT

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When extracting crosscutting concerns from object-oriented systems to aspects, it is often needed to transform the code in order to enable the application of aspects. Although not extensively documented in the literature, object-oriented transformations are critical to turn legacy systems ready to aspect-oriented refactoring. For this reason, in this paper, we provide a set of guidelines for maintainers and developers interested in preparing object-oriented systems to the aspect technology. Moreover, we present a tool that can help developers to identify the need of object-oriented transformations. We also evaluate the proposed guidelines and supporting tool through two case studies.

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“…This process improves system maintainability and evolution and reduces system complexity. It also enables migration from object-oriented to aspect-oriented systems in an efficient way [4][5] [6]. Aspect mining approaches vary according to the type of crosscutting concerns symptoms they search for.…”

Section: Introductionmentioning

confidence: 99%

Clone Detection Using DIFF Algorithm For Aspect Mining

El-Aziz¹,

Aboutabl²,

Mostafa³

2012

IJACSA

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Abstract-Aspect mining is a reverse engineering process that aims at mining legacy systems to discover crosscutting concerns to be refactored into aspects. This process improves system reusability and maintainability. But, locating crosscutting concerns in legacy systems manually is very difficult and causes many errors. So, there is a need for automated techniques that can discover crosscutting concerns in source code. Aspect mining approaches are automated techniques that vary according to the type of crosscutting concerns symptoms they search for. Code duplication is one of such symptoms which risks software maintenance and evolution. So, many code clone detection techniques have been proposed to find this duplicated code in legacy systems. In this paper, we present a clone detection technique to extract exact clones from object-oriented source code using Differential File Comparison Algorithm (DIFF) to improve system reusability and maintainability which is a major objective of aspect mining.

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A Survey of Automated Code-Level Aspect Mining Techniques

Cited by 50 publications

References 19 publications

Variability Mining: Consistent Semi-automatic Detection of Product-Line Features

Variability Mining: Consistent Semi-automatic Detection of Product-Line Features

Guidelines for Enabling the Extraction of Aspects from Existing Object-Oriented Code.

Clone Detection Using DIFF Algorithm For Aspect Mining

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