Using Dataflow Information for Concern Identification in Object-Oriented Software Systems

Trifu, Mircea

doi:10.1109/csmr.2008.4493314

Cited by 17 publications

(8 citation statements)

References 16 publications

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“…It starts from a set of related variables and uses static dataflow information to determine the concern skeleton, a data-oriented abstraction of a concern [27].…”

Section: Conducting the Systematic Reviewmentioning

confidence: 99%

A systematic review on mining techniques for crosscutting concerns

Durelli¹,

Santibanez

Anquetil

et al. 2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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Background: The several maintenance tasks a system is submitted during its life usually cause its architecture deviates from the original conceivable design, ending up with scattered and tangled concerns across the software. The research area named concern mining attempts to identify such scattered and tangled concerns to support maintenance and reverse-engineering. Objectives: The aim of this paper is threefold: (i) identifying techniques employed in this research area, (ii) extending a taxonomy available on the literature and (iii) recommending an initial combination of some techniques. Results: We selected 62 papers by their mining technique. Among these papers, we identified 18 mining techniques for crosscutting concern. Based on these techniques, we have extended a taxonomy available in the literature, which can be used to position each new technique, and to compare it with the existing ones along relevant dimensions. As consequence, we present some combinations of these techniques taking into account high values of precision and recall that could improve the identification of both Persistence and Observer concerns. The combination that we recommend may serve as a roadmap to potential users of mining techniques for crosscutting concerns.

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“…It starts from a set of related variables and uses static dataflow information to determine the concern skeleton, a data-oriented abstraction of a concern [27].…”

Section: Conducting the Systematic Reviewmentioning

confidence: 99%

A systematic review on mining techniques for crosscutting concerns

Durelli¹,

Santibanez

Anquetil

et al. 2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

View full text Add to dashboard Cite

show abstract

“…Textual approaches [14][15] [17] [18] analyze words in source code using IR techniques. Static analysis [23] examines structural information such as control and data dependencies. Dynamic analysis [9][25] [26] examines execution traces of featurespecific execution scenarios.…”

Section: Related Workmentioning

confidence: 99%

Feature Location in a Collection of Product Variants

Xue

Xing

Jarzabek

2012

2012 19th Working Conference on Reverse Engineering

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Companies often develop and maintain a collection of product variants that share some common features but also support different, customer-specific features. To reengineering such legacy product variants for systematic reuse, one must identify features and their implementing code units (e.g. functions, files) in different product variants. Information retrieval (IR) techniques may be applied for that purpose. In this paper, we discuss problems that hinder direct application of IR techniques to a collection of product variants. To counter these problems, we present an approach to support effective feature location in product variants. The novelty of our approach is that we exploit commonalities and differences of product variants by software differencing and FCA techniques so that IR technique can achieve satisfactory results for feature location in product variants. We have implemented our approach and conducted evaluation with a collection of nine Linux kernel product variants. Our evaluation shows that our approach always significantly outperforms a direct application of IR technique in the subject product variants.

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“…While ASDGs and topology analysis use both control and data dependencies to some extent, Trifu [214] introduced an approach to feature location based only on dataflow. …”

Section: Static Feature Locationmentioning

confidence: 99%

“…These dynamic techniques are prone to 1) being noisy because of the difficulty of only invoking the feature of interest at runtime and 2) incomplete because all of a feature's relevant source code may not be executed in a trace. Static feature location techniques generally require more user input and involve a programmer exploring the structural dependencies of code known to implement a feature to find additional relevant code [39,176,214].…”

Section: Introductionmentioning

confidence: 99%

Supporting Feature-Level Software Maintenance

Revelle

2009

2009 16th Working Conference on Reverse Engineering

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Software maintenance is the process of modifying a software system to fix defects, improve performance, add new functionality, or adapt the system to a new environment. A maintenance task is often initiated by a bug report or a request for new functionality. Bug reports typically describe problems with incorrect behaviors or functionalities. These behaviors or functionalities are known as features. Even in very well-designed systems, the source code that implements features is often not completely modularized. The delocalized nature of features makes maintaining them challenging. Since maintenance tasks are expressed in terms of features, the goal of this dissertation is to support software maintenance at the feature-level. We focus on two tasks in particular: feature location and impact analysis via feature coupling.Feature location is the process of identifying the source code that implements a feature, and it is an essential first step to any maintenance task. There are many existing techniques for feature location that incorporate various types of analyses such as static, dynamic, and textual. In this dissertation, we recognize the advantages of leveraging several types of analyses and introduce a new approach to feature location based on combining dynamic analysis, textual analysis, and web mining algorithms applied to software. The use of web mining for feature location is a novel contribution, and we show that our new techniques based on web mining are significantly more effective than the current state of the art.After using feature location to identify a feature's source code, maintenance can be completed on that feature. Impact analysis should then be performed to revalidate the system and determine which other features may have been affected by the modifications. We define three feature coupling metrics that capture the relationship between features based on structural information, textual information, and their combination. Our novel feature coupling metrics can be used for impact analysis to quantify the strength of coupling between pairs of features. We performed three empirical studies on open-source software systems to asses the feature coupling metrics and established three major results. First,

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Using Dataflow Information for Concern Identification in Object-Oriented Software Systems

Cited by 17 publications

References 16 publications

A systematic review on mining techniques for crosscutting concerns

A systematic review on mining techniques for crosscutting concerns

Feature Location in a Collection of Product Variants

Supporting Feature-Level Software Maintenance

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