Open-Source Change Logs

Chen, Kai; Schach, Stephen R.; Yu, Liguo; Offutt, Jeff; Heller, Gillian Z.

doi:10.1023/b:emse.0000027779.70556.d0

Cited by 41 publications

(29 citation statements)

References 3 publications

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“…For example, a survey of maintenance managers yielded the result that 17.4 percent of maintenance is corrective in nature, while a separate study based on analyzing changes to source code led to a result three times larger [52]. Similarly, a comparison between change logs for three software products and the corresponding changed source code itself showed that up to 80% of changes made to the source code were omitted from change logs [8].…”

Section: A1 Software Metricsmentioning

confidence: 99%

The Linux kernel as a case study in software evolution

Israeli

Feitelson

2010

Journal of Systems and Software

111

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We use 810 versions of the Linux kernel, released over a period of 14 years, to characterize the system's evolution, using Lehman's laws of software evolution as a basis. We investigate different possible interpretations of these laws, as reflected by different metrics that can be used to quantify them. For example, system growth has traditionally been quantified using lines of code or number of functions, but functional growth of an operating system like Linux can also be quantified using the number of system calls. In addition we use the availability of the source code to track metrics, such as McCabe's cyclomatic complexity, that have not been tracked across so many versions previously. We find that the data supports several of Lehman's laws, mainly those concerned with growth and with the stability of the process. We also make some novel observations, e.g. that the average complexity of functions is decreasing with time, but this is mainly due to the addition of many small functions.

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Section: A1 Software Metricsmentioning

confidence: 99%

The Linux kernel as a case study in software evolution

Israeli

Feitelson

2010

Journal of Systems and Software

111

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“…Chen et al [20] studied the change logs of three OSS projects and analyzed the quality of these log files.…”

Section: B Quality In Software Engineeringmentioning

confidence: 99%

When process data quality affects the number of bugs: Correlations in software engineering datasets

Bachmann

Bernstein

2010

2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)

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Software engineering process information extracted from version control systems and bug tracking databases are widely used in empirical software engineering. In prior work, we showed that these data are plagued by quality deficiencies, which vary in its characteristics across projects. In addition, we showed that those deficiencies in the form of bias do impact the results of studies in empirical software engineering. While these findings affect software engineering researchers the impact on practitioners has not yet been substantiated. In this paper we, therefore, explore (i) if the process data quality and characteristics have an influence on the bug fixing process and (ii) if the process quality as measured by the process data has an influence on the product (i.e., software) quality. Specifically, we analyze six Open Source as well as two Closed Source projects and show that process data quality and characteristics have an impact on the bug fixing process: the high rate of empty commit messages in Eclipse, for example, correlates with the bug report quality. We also show that the product quality --measured by number of bugs reported --is affected by process data quality measures. These findings have the potential to prompt practitioners to increase the quality of their software process and its associated data quality. Abstract-Software engineering process information extracted from version control systems and bug tracking databases are widely used in empirical software engineering. In prior work, we showed that these data are plagued by quality deficiencies, which vary in its characteristics across projects. In addition, we showed that those deficiencies in the form of bias do impact the results of studies in empirical software engineering. While these findings affect software engineering researchers the impact on practitioners has not yet been substantiated. In this paper we, therefore, explore (i) if the process data quality and characteristics have an influence on the bug fixing process and (ii) if the process quality as measured by the process data has an influence on the product (i.e., software) quality. Specifically, we analyze six Open Source as well as two Closed Source projects and show that process data quality and characteristics have an impact on the bug fixing process: the high rate of empty commit messages in Eclipse, for example, correlates with the bug report quality. We also show that the product quality -measured by number of bugs reported -is affected by process data quality measures. These findings have the potential to prompt practitioners to increase the quality of their software process and its associated data quality.

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“…Incomplete or erroneous records: Chen et al [29] found that in three different OSS systems studied, the omissions in change-logs ranged from 3 to almost 80 percent and conclude that change-logs are not a reliable data source for researchers. This is obviously a concern because some studies may use change-logs as a data source.…”

Section: Threats To Validitymentioning

confidence: 99%