On evaluating the efficiency of software feature development using algebraic manifolds

Kothari, Jay; Bespalov, Dmitriy; Mancoridis, Spiros; Shokoufandeh, Ali

doi:10.1109/icsm.2008.4658049

“…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

Revelle

¹

,

Gethers

²

,

Poshyvanyk

³

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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“…• Features: Instead of studying the code directly, some studies have picked specific features and followed their implementation throughout the lifetime of the software system. For example, Kothari et al [38] proposed a technique to evaluate the efficiency of software feature development by studying the evolution of call graphs generated during the execution of these features. Our study is similar to this work, except for using CI instead of call graphs.…”

Section: Non-code-based Evolution Studiesmentioning

confidence: 99%

An Exploratory Study of the Evolution of Communicated Information about the Execution of Large Software Systems

Shang

¹

,

Jiang

²

,

Adams

³

et al. 2011

2011 18th Working Conference on Reverse Engineering

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Abstract-A great deal of research in software engineering focuses on understanding the dynamic nature of software systems. Such research makes use of automated instrumentation and profiling techniques after fact, i.e., without considering domain knowledge. In this paper, we turn our attention to another source of dynamic information, i.e., the Communicated Information (CI) about the execution of a software system. Major examples of CI are execution logs and system events. They are generated from statements that are inserted intentionally by domain experts (e.g., developers or administrators) to convey crucial points of interest. The accessibility and domain-driven nature of the CI make it a valuable source for studying the evolution of a software system. In a case study on one large open source and one industrial software system, we explore the concept of CI and its evolution by mining the execution logs of these systems. Our study illustrates the need for better traceability techniques between CI and the Log Processing Apps that analyze the CI. In particular, we find that the CI changes at a rather high rate across versions, leading to fragile Log Processing Apps. 40% to 60% of these changes can be avoided and the impact of 15% to 50% of the changes can be controlled through the use of the robust analysis techniques by Log Processing Apps. We also find that Log Processing Apps that track implementation-level CI (e.g., performance analysis) are more fragile than Log Processing Apps that track domain-level CI (e.g., workload modeling), because the implementation-level CI is often short-lived.

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“…For example, Kothari et al . proposed a technique to evaluate the efficiency of software feature development by studying the evolution of call graphs generated during the execution of these features. Our study is similar to this work, except for using CI instead of call graphs.…”

Section: Discussion and Related Workmentioning

confidence: 99%

An exploratory study of the evolution of communicated information about the execution of large software systems

Shang

¹

,

Jiang

²

,

Adams

³

et al. 2013

J Software Evolu Process

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Abstract-A great deal of research in software engineering focuses on understanding the dynamic nature of software systems. Such research makes use of automated instrumentation and profiling techniques after fact, i.e., without considering domain knowledge. In this paper, we turn our attention to another source of dynamic information, i.e., the Communicated Information (CI) about the execution of a software system. Major examples of CI are execution logs and system events. They are generated from statements that are inserted intentionally by domain experts (e.g., developers or administrators) to convey crucial points of interest. The accessibility and domain-driven nature of the CI make it a valuable source for studying the evolution of a software system. In a case study on one large open source and one industrial software system, we explore the concept of CI and its evolution by mining the execution logs of these systems. Our study illustrates the need for better traceability techniques between CI and the Log Processing Apps that analyze the CI. In particular, we find that the CI changes at a rather high rate across versions, leading to fragile Log Processing Apps. 40% to 60% of these changes can be avoided and the impact of 15% to 50% of the changes can be controlled through the use of the robust analysis techniques by Log Processing Apps. We also find that Log Processing Apps that track implementation-level CI (e.g., performance analysis) are more fragile than Log Processing Apps that track domain-level CI (e.g., workload modeling), because the implementation-level CI is often short-lived.

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On evaluating the efficiency of software feature development using algebraic manifolds

Cited by 6 publications

References 24 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 of the Evolution of Communicated Information about the Execution of Large Software Systems

An exploratory study of the evolution of communicated information about the execution of large software systems

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