Relating Identifier Naming Flaws and Code Quality: An Empirical Study

Butler, Simon; Wermelinger, Michel; Yu, Yijun; Sharp, Helen

doi:10.1109/wcre.2009.50

Cited by 78 publications

(65 citation statements)

References 7 publications

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“…Source code is a common input for of traceability approaches [Antoniol et al(2002) Developers normally use identifiers [Butler et al(2009)Butler, Wermelinger, Yu, and Sharp] that are easy to remember. However, these identifiers possibly do not represent concepts in the source code and-or system domain.…”

Section: Source Codementioning

confidence: 99%

“…Software quality depends on identifiers' quality [Butler et al(2009) [Takang et al(1996)Takang, Grubb, and Macredie] compared abbreviated identifiers with full-word identifiers and uncommented code with commented code and empirically analysed the role played by identifiers and comments on source code understandability. They showed that (1) commented systems are more understandable than non-commented systems and, similarly, that (2) systems containing full-word identifiers are more understandable than those with abbreviated identifiers.…”

Section: Identifiers' Qualitymentioning

confidence: 99%

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Factors Impacting the Inputs of Traceability Recovery Approaches

Ali

Guéhéneuc

Antoniol

2011

Software and Systems Traceability

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In requirement engineering, researchers have proposed various tractability recovery approaches. To the best of our knowledge, all traceability recovery approaches have low precision and recall. Our main claim in this chapter is that there exist factors that impact the traceability approaches' inputs, in particular source document, target document, and experts' opinion, that cause low precision and recall. In this chapter, we pursue four objectives: first, to identify and document factors that impact traceability recovery approaches' inputs; second, to identify metrics/tools to measure/improve the quality of the inputs with respect to the identified factors, third, to provide precautions to control these factors, and, fourth, to empirically prove and quantify the effect of one of these factors-expert's programming knowledge-on the traceability recovery approaches' inputs. To achieve the first two objectives, we perform an incremental literature review of traceability recovery approaches and identify and document three key inputs and the seven factors impacting these inputs, out of 12 identified factors. We analyse the reported results in literature for the identified factors to address our third objective. We conduct an empirical study to assess the impact of expert's programming knowledge, to address our fourth objective. We use the effort, number of correct answers, and time to measure the effect of expert's programming knowledge on traceability recovery. We conclude that, in the literature, seven factors impacting the inputs of traceability recovery approaches have been identified, documented, and reported along with related metrics/tools and precautions. We suggest that practitioners should be wary of these seven factors and researchers should focus on the five others to improve traceability recovery approaches.

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Section: Source Codementioning

confidence: 99%

Section: Identifiers' Qualitymentioning

confidence: 99%

Factors Impacting the Inputs of Traceability Recovery Approaches

Ali

Guéhéneuc

Antoniol

2011

Software and Systems Traceability

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“…Butler et al [6] study the effect of identifier names on the quality of code. They find that identifiers that violate certain guidelines have lower code quality (more bug patterns) than ones that don't.…”

Section: Related Workmentioning

confidence: 99%

An Eye Tracking Study on camelCase and under_score Identifier Styles

Sharif

Maletic

2010

2010 IEEE 18th International Conference on Program Comprehension

110

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Abstract-An empirical study to determine if identifiernaming conventions (i.e., camelCase and under_score) affect code comprehension is presented. An eye tracker is used to capture quantitative data from human subjects during an experiment. The intent of this study is to replicate a previous study published at ICPC 2009 (Binkley et al.) that used a timed response test method to acquire data. The use of eye-tracking equipment gives additional insight and overcomes some limitations of traditional data gathering techniques. Similarities and differences between the two studies are discussed. One main difference is that subjects were trained mainly in the underscore style and were all programmers. While results indicate no difference in accuracy between the two styles, subjects recognize identifiers in the underscore style more quickly.

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“…Thus, naming conventions are crucial for improving the source code understandability. Butler et al [10] analyzed the impact of naming conventions on maintenance effort, i.e., on code quality. They evaluated the quality of identifiers in eight open-source Java libraries using 12 naming conventions.…”

Section: Identifiers and Program Comprehensionmentioning

confidence: 99%

“…However, we believe that fault proneness is a complex phenomenon hardly captured solely by structural characteristics of code entities. Indeed, several studies showed that identifiers impact program comprehension (e.g., [5], [6], [7], [8]) and code quality (e.g., [3], [9], [10]). We concur with Deißenböck and Pizka's observation that proper identifiers improve quality and that identifiers should be used consistently [6].…”

Section: Introductionmentioning

confidence: 99%

Physical and conceptual identifier dispersion: Measures and relation to fault proneness

Arnaoudova

Eshkevari

Oliveto

et al. 2010

2010 IEEE International Conference on Software Maintenance

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Abstract-Poorly-chosen identifiers have been reported in the literature as misleading and increasing the program comprehension effort. Identifiers are composed of terms, which can be dictionary words, acronyms, contractions, or simple strings. We conjecture that the use of identical terms in different contexts may increase the risk of faults. We investigate our conjecture using a measure combining term entropy and term context coverage to study whether certain terms increase the odds ratios of methods to be fault-prone. Entropy measures the physical dispersion of terms in a program: the higher the entropy, the more scattered across the program the terms. Context coverage measures the conceptual dispersion of terms: the higher their context coverage, the more unrelated the methods using them. We compute term entropy and context coverage of terms extracted from identifiers in Rhino 1.4R3 and ArgoUML 0.16. We show statistically that methods containing terms with high entropy and context coverage are more fault-prone than others.

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Relating Identifier Naming Flaws and Code Quality: An Empirical Study

Cited by 78 publications

References 7 publications

Factors Impacting the Inputs of Traceability Recovery Approaches

Factors Impacting the Inputs of Traceability Recovery Approaches

An Eye Tracking Study on camelCase and under_score Identifier Styles

Physical and conceptual identifier dispersion: Measures and relation to fault proneness

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