Identifying high-level dependence structures using slice-based dependence analysis

Li, Zheng

doi:10.1109/icsm.2009.5306275

Cited by 6 publications

(7 citation statements)

References 103 publications

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“…Further, lexical information is not used to compute similarities among pairs of classes. The application of link analysis algorithm in a related context to concept location was also explored by Li (2009). Concept extension, concept abbreviation, and concept refinement are targeted in that work by using slicing (for Concept Extension and Concept Abbreviation) and graph analysis (i.e., Vertex Rank Model based on page rank for Concept Refinement).…”

Section: Searching Engines For Source Code and Traceability Link Recomentioning

confidence: 99%

Link analysis algorithms for static concept location: an empirical assessment

2014

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During software evolution, one of the most important comprehension activities is concept location in source code, as it identifies the places in the code where changes are to be made in response to a modification request. Change requests (such as, bug fixing or new feature requests) are usually formulated in natural language, while the source code also includes large amounts of text. In consequence, many of the existing concept location techniques are based on text search or text retrieval. Such approaches reformulate concept location as a document retrieval problem. We refine and improve such solutions by leveraging dependencies between source code elements. Dependency information is used by a link analysis algorithm to rank the document space and to improve concept location based on text retrieval. We implemented our solution to concept location using the PageRank algorithm, used in web document retrieval applications. The results of an empirical evaluation indicate that the new approach leads to better retrieval performance than baseline approaches that use text retrieval and clustering. In addition, we present the results of a controlled experiment and of a differentiated replication to assess whether the new technique supports users in identifying the places in the code where changes are to be made. The results of these experiments revealed that the users exploiting our technique were significantly better supported in the identification of the code to be changed in response to a bug fixing request compared to the users who did not use this technique

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Section: Searching Engines For Source Code and Traceability Link Recomentioning

confidence: 99%

Link analysis algorithms for static concept location: an empirical assessment

2014

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“…Nodes represent methods, and edges correspond to relationships or calls among methods. Therefore, web mining algorithms can be naturally applied to software to discover useful information from its structure, such as key classes for program comprehension [239], component ranks in software repositories [106], and statements that can be refined from concept bindings [127].…”

Section: Dependence Information From Web Miningmentioning

confidence: 99%

“…Components that are generic and frequently reused are ranked highly. Li [127] also uses a variant of PageRank called Vertex Rank Model (VRM) to refine concept bindings found using HB-CA. The VRM works on a dependence graph of concept bindings to identify statements that can be removed from the concept bindings without losing domain knowledge.…”

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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“…Nodes represent methods, and edges correspond to relationships or dependencies among methods. Therefore, web mining algorithms can be naturally applied to software to discover useful information from its structure, such as key classes for program comprehension [32], component ranks in software repositories [19], and statements that can be refined from concept bindings [21]. This work explores whether web mining can also be applied to feature location, either as a standalone technique or used as a filter to an existing approach to feature location.…”

Section: Dependence Information From Web Miningmentioning

confidence: 99%

“…Components that are generic and frequently reused are ranked highly. Li [21] also used a variant of PageRank called Vertex Rank Model (VRM) to refine concept bindings found using HB-CA. The VRM works on a dependence graph of concept bindings to identify statements that can be removed from the concept bindings without losing domain knowledge.…”

Section: Related Workmentioning

confidence: 99%

Using Data Fusion and Web Mining to Support Feature Location in Software

Revelle

Dit

Poshyvanyk

2010

2010 IEEE 18th International Conference on Program Comprehension

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Abstract-Data fusion is the process of integrating multiple sources of information such that their combination yields better results than if the data sources are used individually. This paper applies the idea of data fusion to feature location, the process of identifying the source code that implements specific functionality in software. A data fusion model for feature location is presented which defines new feature location techniques based on combining information from textual, dynamic, and web mining analyses applied to software. A novel contribution of the proposed model is the use of advanced web mining algorithms to analyze execution information during feature location. The results of an extensive evaluation indicate that the new feature location techniques based on web mining improve the effectiveness of existing approaches by as much as 62%.

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Identifying high-level dependence structures using slice-based dependence analysis

Cited by 6 publications

References 103 publications

Link analysis algorithms for static concept location: an empirical assessment

Link analysis algorithms for static concept location: an empirical assessment

Supporting Feature-Level Software Maintenance

Using Data Fusion and Web Mining to Support Feature Location in Software

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