Reducing Program Comprehension Effort in Evolving Software by Recognizing Feature Implementation Convergence

Kothari, Jay; Denton, Trip; Shokoufandeh, Ali; Mancoridis, Spiros

doi:10.1109/icpc.2007.33

Cited by 10 publications

(8 citation statements)

References 11 publications

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“…The association graph matching similarity measure (AGM) introduced by Kothari et al (Kothari et al 2006) is a measure of pair-wise similarity between features based on dynamic call graphs. It has been used to find canonical feature sets (Kothari et al 2006), feature version similarity (Kothari et al 2008), and feature implementation overlap (Kothari et al 2007). …”

Section: Dynamic Coupling Measuresmentioning

confidence: 99%

Using structural and textual information to capture feature coupling in object-oriented software

2011

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Previous studies have demonstrated the relationship between coupling and external software quality attributes, such as fault-proneness, and the application of coupling to software maintenance tasks, such as impact analysis. These previous studies concentrate on class coupling. However, there is a growing focus on the study of features in software, and features are often implemented across multiple classes, meaning class-level coupling measures are not applicable. We ask the pertinent question, "Is measuring coupling at the feature-level also useful?" We define new feature coupling metrics based on structural and textual source code information and extend the unified framework for coupling measurement to include these new metrics. We also conduct three extensive case studies to evaluate these new metrics and answer this research question. The first study examines the relationship between feature coupling and fault-proneness, the second assesses feature coupling in the context of impact analysis, and the third study surveys developers to determine if the metrics align with what they consider to be coupled features. All three studies provide evidence that feature coupling metrics are indeed useful new measures that capture coupling at a higher level of abstraction than classes and can be useful for finding bugs, guiding testing effort, and assessing impact of changes.

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Section: Dynamic Coupling Measuresmentioning

confidence: 99%

Using structural and textual information to capture feature coupling in object-oriented software

2011

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“…Structural approaches include [4,11]. Textual approaches utilize such techniques as information retrieval [14,16], independent component analysis [9], and natural language [20].…”

Section: Related Workmentioning

confidence: 99%

An exploratory study on assessing feature location techniques

Revelle

Poshyvanyk

2009

2009 IEEE 17th International Conference on Program Comprehension

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“…These approaches have focused on how programmers develop features [94], a feature-centric environment for source code browsing [188], identifying canonical sets of features [120,121,119], reverse engineering [91], and identifying and refactoring features that need evolution [149]. Our work focuses on locating features' implementations and on determining the relationships between features using coupling.…”

Section: Scope Of This Dissertationmentioning

confidence: 99%

“…Structural approaches [13,39,121,179,176] explore the relationships among classes, methods, and other program elements to locate features. We did not explore a purely structural feature location technique in this work due to the fact that the other approaches we studied ranked methods, and obtaining a ranking from only structural information is difficult.…”

Section: Related Workmentioning

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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Reducing Program Comprehension Effort in Evolving Software by Recognizing Feature Implementation Convergence

Cited by 10 publications

References 11 publications

Using structural and textual information to capture feature coupling in object-oriented software

Using structural and textual information to capture feature coupling in object-oriented software

An exploratory study on assessing feature location techniques

Supporting Feature-Level Software Maintenance

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