Fault-Prone Java Method Analysis Focusing on Pair of Local Variables with Confusing Names

Tashima, Keiichiro; Aman, Hirohisa; Amasaki, Sousuke; Yokogawa, Tomoyuki; Kawahara, Mutsuto

doi:10.1109/seaa.2018.00033

Cited by 6 publications

(14 citation statements)

References 13 publications

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“…To assess all the considered confusing measures, we have performed an empirical study by considering the same software systems employed in [1]. The results confirm and extend the ones of previous study about the relationships between the presence of faults and local variables with confusing names and how fault prediction models (built using the Random Forest) based on the considered distances can provide accurate estimations.…”

Section: Introductionsupporting

confidence: 78%

“…Differently from the above mentioned contributions, Tashima et al [1] have recently focused their attention on pairs of local variables with similar and confusing names. The aim of their investigation is to verify the relationships between the presence of such confusing variables and the fault-proneness at method level.…”

Section: Related Workmentioning

confidence: 99%

“…This paper presents a further investigation about the use of the Normalized Levenshtein Distance (NLD) proposed by Tashima et al [1]. NLD allows to quantify the string similarity between local variables by exploiting the Levenshtein distance.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, it determines the minimum number of modifications in the characters to change one string to another string. NLD allows to quantify the confusion of local variables [1] and verify if the presence of not easily distinguishable variables in a method can suggest that the method is fault prone. In addition, we propose further uses of NLD: NLD 1 , which represents the sum of all the NLD values calculated for each pair of variables declared in a method; NLD 2 , which represents the average of all the NLD values for each pair of local variables; and NLD 3 , which is defined as the sum of all the NLD values for each pair of local variables multiplied by the number of LOCs of the method.…”

Section: Introductionmentioning

confidence: 99%

“…Organization of the paper: In Section 2 we summarize related work and recall the definition of NLD [1]. The design and the results of the performed empirical study are presented in Section 3 and Section 4, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Using the Normalized Levenshtein Distance to Analyze Relationship between Faults and Local Variables with Confusing Names: A further Investigation (S)

Gravino

2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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Section: Introductionsupporting

confidence: 78%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Using the Normalized Levenshtein Distance to Analyze Relationship between Faults and Local Variables with Confusing Names: A further Investigation (S)

Gravino

2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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An automated detection of confusing variable pairs with highly similar compound names in Java and Python programs

et al. 2023

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Variable names represent a significant source of information regarding the source code, and a successful naming of variables is key to producing readable code. Programmers often use a compound variable name by concatenating two or more words to make it more informative and enhance the code readability. While each compound variable name is descriptive, a collection of them sometimes produces “confusing” variable pairs if their names are highly similar, e.g., “shippingHeight,” vs. “shippingWeight.” A confusing variable pair would adversely affect the code readability because it can cause a misreading or mix-up of variables during the programming or code review activities. Toward automated support for enhancing code readability, this paper conducts a large-scale investigation of compound variable names in Java and Python programs. The investigation collects 116,921,127 pairs of compound-named variables from 1,876 open-source Java projects and 106,943,523 pairs of such variables from 2,427 open-source Python projects. Then, this study analyzes those variable pairs from two perspectives of name similarity: string similarity and semantic similarity. Through an evaluation study with 30 human participants, the data analyses show that both string and semantic similarity can help detect confusing variable pairs in Java and Python programs. In order to distill confusing variable pairs automatically, support tools for detecting confusing variable pairs are also developed in this study.

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Some SonarQube issues have a significant but small effect on faults and changes. A large-scale empirical study

Lenarduzzi

Saarimäki

Taibi

2020

Journal of Systems and Software

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Fault-Prone Java Method Analysis Focusing on Pair of Local Variables with Confusing Names

Cited by 6 publications

References 13 publications

Using the Normalized Levenshtein Distance to Analyze Relationship between Faults and Local Variables with Confusing Names: A further Investigation (S)

Using the Normalized Levenshtein Distance to Analyze Relationship between Faults and Local Variables with Confusing Names: A further Investigation (S)

An automated detection of confusing variable pairs with highly similar compound names in Java and Python programs

Some SonarQube issues have a significant but small effect on faults and changes. A large-scale empirical study

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