Recognizing Antipatterns and Analyzing Their Effects on Software Maintainability

Bán, Dénes; Ferenć, Rudolf

doi:10.1007/978-3-319-09156-3_25

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…The study provided examples of new releases of projects to fix the inefficient usage of regular expressions, recurrent computations of constant data, and usage of deprecated decryption algorithms. S14 [53] comprised an analysis of 34 software projects and reported a significant positive correlation between number of bugs and number of anti-patterns. It also reported a negative correlation between number of anti-patterns and maintainability.…”

Section: Do Code Smells Influence the Occurrence Of Software Bugs? Sementioning

confidence: 99%

The Impact of Code Smells on Software Bugs: A Systematic Literature Review

2018

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Context: Code smells are associated to poor design and programming style, which often degrades code quality and hampers code comprehensibility and maintainability. Goal: identify published studies that provide evidence of the influence of code smells on the occurrence of software bugs. Method: We conducted a Systematic Literature Review (SLR) to reach the stated goal. Results: The SLR selected studies from July 2007 to September 2017, which analyzed the source code of open source software projects and several code smells. Based on evidence of 16 studies covered in this SLR, we conclude that 24 code smells are more influential in the occurrence of bugs relative to the remaining smells analyzed. In contrast, three studies reported that at least 6 code smells are less influential in such occurrences. Evidence from the selected studies also point out tools, techniques, and procedures that should be applied to analyze the influence of the smells. Conclusions: To the best of our knowledge, this is the first SLR to target this goal. This study provides an up-to-date and structured understanding of the influence of code smells on the occurrence of software bugs based on findings systematically collected from a list of relevant references in the latest decade.

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Section: Do Code Smells Influence the Occurrence Of Software Bugs? Sementioning

confidence: 99%

The Impact of Code Smells on Software Bugs: A Systematic Literature Review

2018

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“…The Connections between Antipatterns and Maintainability As we mentioned earlier, little research has been done so far on finding an explicit connection between antipatterns and maintainability. One of these is our previous study [4], where the two concepts were inversely related, while antipatterns were proportionately related to program faults (or bugs). Another is an investigation by Fontana and Maggioni [8] where they assume the connection and use antipatterns as well as source code metrics to evaluate software quality.…”

Section: Related Workmentioning

confidence: 95%

“…For the automatic recognition of antipattern instances, we used our previously published static source code analyzer tool [4]. It currently recognizes the 9 types of antipatterns listed below.…”

Section: Antipatternsmentioning

confidence: 99%

The Connection between Antipatterns and Maintainability in Firefox

Bán¹

2017

Acta Cybern

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The notion that antipatterns have a detrimental effect on source code maintainability is widely accepted, but there is relatively little objective evidence to support it. We seek to investigate this issue by analyzing the connection between antipatterns and maintainability in an empirical study of Firefox, an open source browser application developed in C++. After extracting antipattern instances and maintainability information from 45 revisions, we looked for correlations to uncover a connection between the two concepts. We found statistically significant negative values for both Pearson and Spearman correlations, most of which were under -0.65. These values suggest there are strong, inverse relationships, thereby supporting our initial assumption that the more antipatterns the source code contains, the harder it is to maintain. Lastly, we combined these data into a table applicable for machine learning experiments, which we conducted using Weka [10] and several of its classifier algorithms. All five regression types we tried had correlation coefficients over 0.77 and used mostly negative weights for the antipattern predictors in the models we constructed. In conclusion, we can say that this empirical study is another step towards objectively demonstrating that antipatterns have an adverse effect on software maintainability.

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“…Metrics can be obtained from the source code, from the project management system, or even from the execution traces of the source code. We can deduce higher-level software characteristics from lower level ones [44], such as the maintainability of the source code or the distribution of defects, but they can be also used to build a cost estimation model, apply performance optimization, or to improve activities supporting software quality [45,46,47]. In this work, we used static source code metrics (also known as software product metrics).…”

Section: Metricsmentioning

confidence: 99%

An Automatically Created Novel Bug Dataset and its Validation in Bug Prediction

Ferenc,

Gyimesi,

Gyimesi

et al. 2020

Preprint

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Bugs are inescapable during software development due to frequent code changes, tight deadlines, etc.; therefore, it is important to have tools to find these errors. One way of performing bug identification is to analyze the characteristics of buggy source code elements from the past and predict the present ones based on the same characteristics, using e.g. machine learning models.To support model building tasks, code elements and their characteristics are collected in so-called bug datasets which serve as the input for learning.We present the BugHunter Dataset: a novel kind of automatically constructed and freely available bug dataset containing code elements (files, classes, methods) with a wide set of code metrics and bug information. Other available bug datasets follow the traditional approach of gathering the characteristics of all source code elements (buggy and non-buggy) at only one or more pre-selected release versions of the code. Our approach, on the other hand, captures the buggy and the fixed states of the same source code elements from the narrowest timeframe we can identify for a bug's presence, regardless of release versions. To show the usefulness of the new dataset, we built and evaluated bug prediction models and achieved F-measure values over 0.74.

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Recognizing Antipatterns and Analyzing Their Effects on Software Maintainability

Cited by 10 publications

References 12 publications

The Impact of Code Smells on Software Bugs: A Systematic Literature Review

The Impact of Code Smells on Software Bugs: A Systematic Literature Review

The Connection between Antipatterns and Maintainability in Firefox

An Automatically Created Novel Bug Dataset and its Validation in Bug Prediction

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