Understanding the shape of Java software

Baxter, G. J.; FreanMarcus,; Noble, James; RickerbyMark,; SmithHayden,; Visser, Matt; MeltonHayden,; Tempero, Ewan

doi:10.1145/1167515.1167507

Cited by 81 publications

(110 citation statements)

References 18 publications

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“…2 notes the total number of inspected files in each project, while the next three columns show that, while the public repositories not always keep trace of the full development history the project, they still record the bulk of the development 9 . The penultimate column shows the median number of commits per file.…”

Section: The Datasetmentioning

confidence: 99%

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When do Software Complexity Metrics Mean Nothing? – When Examined out of Context.

Gil¹,

Lalouche²

2016

JOT

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This paper places its attention on a familiar phenomena: that code metrics such as lines of code are extremely context dependent and their distribution differs from project to project. We apply visual inspection, as well as statistical reasoning and testing, to show that such metric values are so sensitive to context, that their measurement in one project offers little prediction regarding their measurement in another project.On the positive side, we show that context bias can be neutralized, at least for the majority of metrics that we considered, by what we call Log Normal Standardization (LNS). Concretely, the LNS transformation is obtained by shifting (by subtracting the mean) and scaling (by dividing by the standard deviation) of the log of a metric value.Thus, we conclude that the LNS-transformed-, are to be preferred over the plain-, values of metrics, especially in comparing modules from different projects. Conversely, the LNS-transformation suggests that the "context bias" of a software project with respect to a specific metric can be summarized with two numbers: the mean of the logarithm of the metric value, and its standard deviation.

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Section: The Datasetmentioning

confidence: 99%

“…Also, it should be clear that rank-normalization is applicable to any metric with sufficiently large entropy. Partial extrapolation to method-level metrics can be made relying on data found in the literature [9,39], showing that the distributions of many of these is Pareto or log-normal.…”

Section: Threats To Validitymentioning

confidence: 99%

When do Software Complexity Metrics Mean Nothing? – When Examined out of Context.

Gil¹,

Lalouche²

2016

JOT

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“…Collberg et al [7] performed an in-depth empirical analysis of Java bytecode, computing a wide variety of metrics, including object-oriented metrics (e.g., classes per package, fields per class) and instruction-level metrics (e.g., instruction frequencies, common sequences of instructions). Baxter et al [6] looked at a combination of Java bytecode and Java source (generated from bytecode), where they focused on characterizing the distributions for a number of metrics.…”

Section: Related Workmentioning

confidence: 99%

An empirical study of PHP feature usage: a static analysis perspective

Hills

Klint

Vinju

2013

Proceedings of the 2013 International Symposium on Software Testing and Analysis

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PHP is one of the most popular languages for server-side application development. The language is highly dynamic, providing programmers with a large amount of flexibility. However, these dynamic features also have a cost, making it difficult to apply traditional static analysis techniques used in standard code analysis and transformation tools. As part of our work on creating analysis tools for PHP, we have conducted a study over a significant corpus of open-source PHP systems, looking at the sizes of actual PHP programs, which features of PHP are actually used, how often dynamic features appear, and how distributed these features are across the files that make up a PHP website. We have also looked at whether uses of these dynamic features are truly dynamic or are, in some cases, statically understandable, allowing us to identify specific patterns of use which can then be taken into account to build more precise tools. We believe this work will be of interest to creators of analysis tools for PHP, and that the methodology we present can be leveraged for other dynamic languages with similar features.

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“…Studies and analyses.Baxter et al presented the first-in-depth study on the structure of Java programs [2]. In their study, they analyzed 56 Java applications and measured several key structural attributes (e.g., number of methods, number of fields etc.)…”

Section: Related Workmentioning

confidence: 99%

An empirical investigation into a large-scale Java open source code repository

Grechanik

McMillan

DeFerrari

et al. 2010

Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

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Getting insight into different aspects of source code artifacts is increasingly important -yet there is little empirical research using large bodies of source code, and subsequently there are not much statistically significant evidence of common patterns and facts of how programmers write source code. We pose 32 research questions, explain rationale behind them, and obtain facts from 2,080 randomly chosen Java applications from Sourceforge. Among these facts we find that most methods have one or zero arguments or they do not return any values, few methods are overridden, most inheritance hierarchies have the depth of one, close to 50% of classes are not explicitly inherited from any classes, and the number of methods is strongly correlated with the number of fields in a class.

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Understanding the shape of Java software

Cited by 81 publications

References 18 publications

When do Software Complexity Metrics Mean Nothing? – When Examined out of Context.

When do Software Complexity Metrics Mean Nothing? – When Examined out of Context.

An empirical study of PHP feature usage: a static analysis perspective

An empirical investigation into a large-scale Java open source code repository

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