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The shape of feature code: an analysis of twenty C-preprocessor-based systems.

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Title: The shape of feature code: an analysis of twenty C-preprocessor-based systems.
Authors: Queiroz, Rodrigo1 rodrigoqueiroz@dcc.ufmg.br, Passos, Leonardo2 lnrdpss@acm.org, Valente, Marco1 mtov@dcc.ufmg.br, Hunsen, Claus3 claus.hunsen@uni-passau.de, Apel, Sven3 apel@uni-passau.de, Czarnecki, Krzysztof2 kczarnec@gsd.uwaterloo.ca
Source: Software & Systems Modeling. Feb2017, Vol. 16 Issue 1, p77-96. 20p.
Subjects: Open source software, Ripple (Computer network protocol), Uniform distribution (Probability theory), Standard deviations, Cumulative distribution function
Abstract: Feature annotations (e.g., code fragments guarded by #ifdef C-preprocessor directives) control code extensions related to features. Feature annotations have long been said to be undesirable. When maintaining features that control many annotations, there is a high risk of ripple effects. Also, excessive use of feature annotations leads to code clutter, hinder program comprehension and harden maintenance. To prevent such problems, developers should monitor the use of feature annotations, for example, by setting acceptable thresholds. Interestingly, little is known about how to extract thresholds in practice, and which values are representative for feature-related metrics. To address this issue, we analyze the statistical distribution of three feature-related metrics collected from a corpus of 20 well-known and long-lived C-preprocessor-based systems from different domains. We consider three metrics: scattering degree of feature constants, tangling degree of feature expressions, and nesting depth of preprocessor annotations. Our findings show that feature scattering is highly skewed; in 14 systems (70 %), the scattering distributions match a power law, making averages and standard deviations unreliable limits. Regarding tangling and nesting, the values tend to follow a uniform distribution; although outliers exist, they have little impact on the mean, suggesting that central statistics measures are reliable thresholds for tangling and nesting. Following our findings, we then propose thresholds from our benchmark data, as a basis for further investigations. [ABSTRACT FROM AUTHOR]
Copyright of Software & Systems Modeling is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Queiroz%2C+Rodrigo%22">Queiroz, Rodrigo</searchLink><relatesTo>1</relatesTo><i> rodrigoqueiroz@dcc.ufmg.br</i><br /><searchLink fieldCode="AR" term="%22Passos%2C+Leonardo%22">Passos, Leonardo</searchLink><relatesTo>2</relatesTo><i> lnrdpss@acm.org</i><br /><searchLink fieldCode="AR" term="%22Valente%2C+Marco%22">Valente, Marco</searchLink><relatesTo>1</relatesTo><i> mtov@dcc.ufmg.br</i><br /><searchLink fieldCode="AR" term="%22Hunsen%2C+Claus%22">Hunsen, Claus</searchLink><relatesTo>3</relatesTo><i> claus.hunsen@uni-passau.de</i><br /><searchLink fieldCode="AR" term="%22Apel%2C+Sven%22">Apel, Sven</searchLink><relatesTo>3</relatesTo><i> apel@uni-passau.de</i><br /><searchLink fieldCode="AR" term="%22Czarnecki%2C+Krzysztof%22">Czarnecki, Krzysztof</searchLink><relatesTo>2</relatesTo><i> kczarnec@gsd.uwaterloo.ca</i>
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  Data: <searchLink fieldCode="JN" term="%22Software+%26+Systems+Modeling%22">Software & Systems Modeling</searchLink>. Feb2017, Vol. 16 Issue 1, p77-96. 20p.
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  Data: Feature annotations (e.g., code fragments guarded by #ifdef C-preprocessor directives) control code extensions related to features. Feature annotations have long been said to be undesirable. When maintaining features that control many annotations, there is a high risk of ripple effects. Also, excessive use of feature annotations leads to code clutter, hinder program comprehension and harden maintenance. To prevent such problems, developers should monitor the use of feature annotations, for example, by setting acceptable thresholds. Interestingly, little is known about how to extract thresholds in practice, and which values are representative for feature-related metrics. To address this issue, we analyze the statistical distribution of three feature-related metrics collected from a corpus of 20 well-known and long-lived C-preprocessor-based systems from different domains. We consider three metrics: scattering degree of feature constants, tangling degree of feature expressions, and nesting depth of preprocessor annotations. Our findings show that feature scattering is highly skewed; in 14 systems (70 %), the scattering distributions match a power law, making averages and standard deviations unreliable limits. Regarding tangling and nesting, the values tend to follow a uniform distribution; although outliers exist, they have little impact on the mean, suggesting that central statistics measures are reliable thresholds for tangling and nesting. Following our findings, we then propose thresholds from our benchmark data, as a basis for further investigations. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Software & Systems Modeling is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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