Link analysis algorithms for static concept location: an empirical assessment

Scanniello, Giuseppe; Marcus, Andrian; Pascale, Daniele

doi:10.1007/s10664-014-9327-7

Cited by 31 publications

(24 citation statements)

References 68 publications

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“…Structural analysis, often referred to as static analysis, allows developers to identify the relevant program elements by following data or control flow dependencies between them. For example, if one procedure (or method) is known to be part of the feature and it is the only caller of another procedure, then it is considered likely that this latter procedure is also part of the feature (Bassett and Kraft 2013;Scanniello et al 2015).…”

Section: Classification Of Fltsmentioning

confidence: 99%

“…Also, original FLTs have been gradually refined with the intention of enhancing their efficacy. Scanniello et al (2015), Saha et al (2013), Revelle et al (2011), and Rao and Kak (2011), Poshyvanyk et al (2006Poshyvanyk et al ( , 2012, Panichella et al (2013), Mahmoud and Bradshaw (2015), Kagdi et al (2013), Heck and Zaidman (2014), Cleary et al (2009), Chochlov et al (2017), Binkley et al (2015), Bassett and Kraft (2013), and Ali et al (2013).…”

Section: Introductionmentioning

confidence: 99%

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An empirical assessment of baseline feature location techniques

Razzaq

Gear²,

Exton

et al. 2019

Empir Software Eng

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Feature Location (FL) aims to locate observable functionalities in source code. Considering its key role in software maintenance, a vast array of automated and semi-automated Feature Location Techniques (FLTs) have been proposed. To compare FLTs, an open, standard set of non-subjective, reproducible “compare-to” FLT techniques (baseline techniques) should be used for evaluation. In order to relate the performance of FLTs compared against different baseline techniques, these compare-to techniques should be evaluated against each other. But evaluation across FLTs is confounded by empirical designs that incorporate different FL goals and evaluation criteria. This paper moves towards standardizing FLT comparability by assessing eight baseline techniques in an empirical design that addresses these confounding factors. These baseline techniques are assessed in twelve case studies to rank their performance. Results of the case studies suggest that different baseline techniques perform differently and that VSM-Lucene and LSI-Matlab performed better than other implementations. By presenting the relative performances of baseline techniques this paper facilitates empirical cross-comparison of existing and future FLTs. Finally, the results suggest that the performance of FLTs partially depends on system/benchmark characteristics, in addition to the FLTs themselves.

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Section: Classification Of Fltsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An empirical assessment of baseline feature location techniques

Razzaq

Gear²,

Exton

et al. 2019

Empir Software Eng

View full text Add to dashboard Cite

show abstract

“…Static analysis allows developers to identify the relevant software entities by the data or control flow dependencies between them [46]. For example, if one software entity is known to be part of the concept and it is the only caller of another entity, then it is considered likely that this latter entity is also part of the concept [51], [52]. Dynamic analysis refers to the invocation and observation of concept at execution time: execution traces are analyzed to identify code that is always executed when the concept is exercised in the system, and code that is not executed when the concept is not, thus identifying code that is (exclusively) associated with the concept [53], [54].…”

Section: Identifying the Starting Impact Setmentioning

confidence: 99%

Change-Patterns Mapping: A Boosting Way for Change Impact Analysis

Huang

Jinyu

Luo

et al. 2022

IIEEE Trans. Software Eng.

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Change impact analysis (CIA) is a specialized process of program comprehension that investigates the ripple effects of a code change in a software system. In this paper, we present a boosting way for change impact analysis via mapping the historical change-patterns to current CIA task in a cross-project scenario. The change-patterns reflect the coupling dependencies between changed entities in a change set. A traditional CIA tool (such as ImpactMiner) outputs an initial impact set for a starting entity. To boost the traditional CIA tool, our approach retrieves an equivalent entity from various historical change sets for the starting entity. Then, the change-patterns between the equivalent entity and the rest of entities in the change set are mapped to the CIA task at hand. For current CIA task, if an entity in the initial impact set involves the similar change-pattern with the starting entity when comparing with the mapped change-pattern, we will reward the impacted confidence of the entity. Accuracy improvements are observed in the experiments when applying our boosting method to three famous CIA tools, i.e., ImpactMiner, JRipples and ROSE.

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“…Existing CIA technologies fall into four categories [2]: static [14,15], dynamic [16,17], textual [18,19], and hybrid [20,21].…”

Section: Introductionmentioning

confidence: 99%

A Structure-Driven Method for Information Retrieval-Based Software Change Impact Analysis

Wang

et al. 2018

Scientific Programming

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An important application of information retrieval technology is software change impact analysis. Existing information retrieval-based change impact analysis methods select a single method to transform the source code corpus into vectors in a process known as indexing. The single method is chosen from two primary methods, known as the bag-of-words and word embedding models, each having their specific advantages and disadvantages. The bag-of-words model records every word in the source code but ignores contextual information in the corpus. The word embedding model records the contextual information but loses detail for individual words. To address this problem, we propose a structure-driven method for information retrieval-based change impact analysis (named SDM-CIA). SDM-CIA integrates the bag-of-words and word embedding models based on the software’s structure. Our experiments using a standard benchmark shows that when compared with the existing methods, SDM-CIA improves on precision performance, recall performance, F-score performance, and MRR performance by an average of 3.65%, 3.82%, 3.6%, and 10.28%, respectively. Our experiments confirm the effectiveness of SDM-CIA.

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Link analysis algorithms for static concept location: an empirical assessment

Cited by 31 publications

References 68 publications

An empirical assessment of baseline feature location techniques

An empirical assessment of baseline feature location techniques

Change-Patterns Mapping: A Boosting Way for Change Impact Analysis

A Structure-Driven Method for Information Retrieval-Based Software Change Impact Analysis

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