Static and dynamic distance metrics for feature-based code analysis

Wong, W. Eric; Gokhale, Swapna S.

doi:10.1016/j.jss.2004.02.029

Cited by 17 publications

(17 citation statements)

References 14 publications

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“…Currently the only existing feature-level coupling-like metric that we are aware of is based on dynamic information. Wong and Gokhale (Wong and Gokhale 2005) defined the distance (DIST) between two features using an execution slice-based technique. Similar feature metrics have been proposed to dynamically measure certain relationships or dependencies between features (Greevy and Ducasse 2005;Lienhard et al 2007) other than coupling.…”

Section: Dynamic Coupling Measuresmentioning

confidence: 99%

“…However, dynamic information may not be sufficient to precisely capture coupling among features. The best way to collect dynamic information is to execute scenarios that exercise only one feature at a time, but developing such scenarios can be difficult, if possible at all (Wong and Gokhale 2005). Our metrics are the first to capture feature coupling using structural and textual information, thus avoiding the overhead of collecting execution traces.…”

Section: Dynamic Coupling Measuresmentioning

confidence: 99%

“…The distance between features metric (DIST) introduced by Wong and Gokhale (Wong and Gokhale 2005) is a feature metric that is very similar to coupling because it measures the distance (or similarity) between features. DIST is computed based on information collected by dynamically executing a system.…”

Section: Comparison With An Existing Metricmentioning

confidence: 99%

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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%

Section: Dynamic Coupling Measuresmentioning

confidence: 99%

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

2011

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“…Additionally, the scenarios used to collect traces may not invoke all of the code that is relevant to the feature, meaning some of the feature's implementation may not be located [233]. Conversely, it may be difficult to formulate a scenario that invokes only the desired feature, causing irrelevant code to be executed [68,76,233]. Dynamic feature location techniques are discussed in Section 2.2.1.…”

Section: Type Of Analysismentioning

confidence: 99%

“…Currently the only existing feature-level coupling-like metric that we are aware of is based on dynamic information. Wong and Gokhale [233] defined the distance (DIST ) between two features using an execution slice-based technique. Similar feature metrics have been proposed to dynamically measure certain relationships or dependencies between features [92,128] other than coupling.…”

Section: Dynamic Coupling Measuresmentioning

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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Industrial tools for the feature location problem: an exploratory study

Simmons

Edwards

Wilde

et al. 2006

J. Softw. Maint. Evol.: Res. Pract.

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Software engineers who maintain and enhance large systems often encounter the feature location problem: where in the many thousands of lines of code is a particular user feature implemented? Several methods of addressing the problem have been proposed, most of which involve tracing the execution of the system and analyzing the traces. Some supporting academic tools are available. However, companies that depend on the successful evolution of large systems are more likely to use new methods if they are supported by industrial‐strength tools of known reliability. This article describes a study performed with Motorola, Inc. to see whether there were any pitfalls in using Metrowerks CodeTEST and Klocwork inSight for feature location on message‐passing software similar to systems that Motorola maintains. These two tools were combined with TraceGraph, an academic trace comparison tool. The study identified two main problems. First, some ‘glue’ code and workarounds were needed to get CodeTEST to generate a trace for an interval of time in which the feature was operating. Second, getting information out of TraceGraph and into inSight was needlessly complicated for a user. However, with a moderate amount of work, the tool combination was effective in locating, understanding and documenting features. Study participants completed these steps in typically 3–4 hours per feature, studying only a few hundred lines out of a 200 000 line system. An ongoing project with Motorola is focused on improving tool integration with the hope of making feature location common practice at Motorola. Copyright © 2006 John Wiley & Sons, Ltd.

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Static and dynamic distance metrics for feature-based code analysis

Cited by 17 publications

References 14 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

Supporting Feature-Level Software Maintenance

Industrial tools for the feature location problem: an exploratory study

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