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  1. Proceedings of the The 12th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2016)
  2. Measuring the Stylistic Inconsistency in Software Projects using Hierarchical Agglomerative Clustering
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Forecasting Communication Behavior in Student Software Projects
Estimating Story Points from Issue Reports
Search Based Training Data Selection For Cross Project Defect Prediction
On the Terms Within- and Cross-Company in Software Effort Estimation
Measuring the Stylistic Inconsistency in Software Projects using Hierarchical Agglomerative Clustering
An Empirical Evaluation of Distribution-based Thresholds for Internal Software Measures
Data Sets and Data Quality in Software Engineering: Eight Years On
Hidden Markov Models for the Prediction of Developer Involvement Dynamics and Workload
Predicting the Popularity of GitHub Repositories
A Manually Validated Code Refactoring Dataset and Its Assessment Regarding Software Maintainability

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Measuring the Stylistic Inconsistency in Software Projects using Hierarchical Agglomerative Clustering

Content Provider ACM Digital Library
Author Keung, Jacky Mi, Qing Yu, Yang
Abstract Background: Although many software engineering methodologies and guidelines are provided, it is common that developers apply their very own programming styles to the source code being produced. These individually preferred programming styles are more comprehensive for themselves, but may well conflict with each other. Thus, the problem of stylistic inconsistency is inevitable during the software development process involving multiple developers, the result is undesirable and that will significantly degrade program readability and maintainability. Aims: Given limited understanding in this regard, we perform an empirical analysis for the purpose of quantitatively measuring the inconsistency degree of programming style within a software project team. Method: We first propose stylistic fingerprints, which are represented as a set of attribute-counting-metrics, in an attempt to characterize different programming styles. Then we adopt the hierarchical agglomerative clustering (HAC) technique to quantitatively measuring the proximity of programming style based on six C/C++ open source projects chosen from different application domains. Results: The empirical results demonstrate the feasibility and validity of our fingerprinting methodology. Moreover, the proposed clustering procedure utilizing HAC algorithm with dendrograms is capable of effectively illustrating the inconsistency degree of programming style among source files, which is significant for future research. Conclusions: This study proposed an effective and efficient approach for analyzing programming style inconsistency, supported by a sound theoretical basis for dealing with such a problem. Ultimately improving program readability and therefore reduce the maintenance overhead for software projects.
Starting Page 1
Ending Page 10
Page Count 10
File Format PDF
ISBN 9781450347723
DOI 10.1145/2972958.2972963
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-09-09
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Stylistic inconsistency Empirical software engineering Hierarchical agglomerative clustering Programming style
Content Type Text
Resource Type Article
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