Discovering Patterns of Change Types

Fluri, Beat; Giger, Emanuel; Gall, Harald C.

doi:10.1109/ase.2008.74

Cited by 38 publications

(28 citation statements)

References 10 publications

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“…The granularity of the change patterns are exactly the same as in [6]. On the other hand, Fluri et al [10] used hierarchical clustering and tree differencing to find change pattern but with a very coarsegrained changes like development change and maintenance change.…”

Section: Related Workmentioning

confidence: 99%

Mining frequent bug-fix code changes

Osman

Lungu

Nierstrasz

2014

2014 Software Evolution Week - IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE)

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Abstract-Detecting bugs as early as possible plays an important role in ensuring software quality before shipping. We argue that mining previous bug fixes can produce good knowledge about why bugs happen and how they are fixed.In this paper, we mine the change history of 717 open source projects to extract bug-fix patterns. We also manually inspect many of the bugs we found to get insights into the contexts and reasons behind those bugs. For instance, we found out that missing null checks and missing initializations are very recurrent and we believe that they can be automatically detected and fixed.

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Section: Related Workmentioning

confidence: 99%

Mining frequent bug-fix code changes

Osman

Lungu

Nierstrasz

2014

2014 Software Evolution Week - IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE)

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“…This is because they might are moved into a new else-part or because a new local variable is needed to handle the different conditions. In [12], Fluri et al attempt to find an explanation why certain change types occur more frequently together than others, i.e., why they correlate.…”

Section: Correlation Of Scc Categoriesmentioning

confidence: 99%

Comparing fine-grained source code changes and code churn for bug prediction

Giger

Pinzger

Gall

2011

Proceedings of the 8th Working Conference on Mining Software Repositories

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A significant amount of research effort has been dedicated to learning prediction models that allow project managers to efficiently allocate resources to those parts of a software system that most likely are bug-prone and therefore critical. Prominent measures for building bug prediction models are product measures, e.g., complexity or process measures, such as code churn. Code churn in terms of lines modified (LM) and past changes turned out to be significant indicators of bugs. However, these measures are rather imprecise and do not reflect all the detailed changes of particular source code entities during maintenance activities. In this paper, we explore the advantage of using fine-grained source code changes (SCC) for bug prediction. SCC captures the exact code changes and their semantics down to statement level. We present a series of experiments using different machine learning algorithms with a dataset from the Eclipse platform to empirically evaluate the performance of SCC and LM. The results show that SCC outperforms LM for learning bug prediction models.

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“…al. [4], proposed an semi-automated approach using agglomerative hierarchical clustering to discover patterns from source code changes types. Fenton et.…”

Section: Related Workmentioning

confidence: 99%

Application of Data Mining Techniques for Defect Detection and Classification

Prakash¹,

Ashoka²,

Aradya³

2015

Advances in Intelligent Systems and Computing

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Abstract. In order to overcome the software development challenges like delivering a project on time`, developing quality software products and reducing development cost, software industries commonly uses defect detection software tools to manage quality in software products. Defects are detected and classified based on their severity, this can be automated in order to reduce the development time and cost. Nowadays to extract useful knowledge from large software repositories engineers and researchers are using data mining techniques. In this paper, software defect detection and classification method is proposed and data mining techniques are integrated to identify, classify the defects from large software repository. Based on defects severity proposed method discussed in this paper focuses on three layers: core, abstraction and application layer. The designed method is evaluated using the parameters precision and recall.

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Discovering Patterns of Change Types

Cited by 38 publications

References 10 publications

Mining frequent bug-fix code changes

Mining frequent bug-fix code changes

Comparing fine-grained source code changes and code churn for bug prediction

Application of Data Mining Techniques for Defect Detection and Classification

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