Tool Support for Continuous Quality Control

Deissenboeck, Florian; Juergens, Elmar; Hummel, Benjamin; Wagner, Stefan; Parareda, Benedikt Mas y; Pizka, Markus

doi:10.1109/ms.2008.129

Cited by 64 publications

(33 citation statements)

References 8 publications

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“…Using the analysis tool ConQAT [19], we found that the whole Tomcat 6.0.0 source distribution contains 306,675 LOC of Java code, which correspond to 151,509 SLOC in 1030 files. This is the size baseline for all the following densities.…”

Section: Resultsmentioning

confidence: 99%

A Bayesian network approach to assess and predict software quality using activity-based quality models

Wagner

2010

Information and Software Technology

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Context: Software quality is a complex concept. Therefore, assessing and predicting it is still challenging in practice as well as in research. Activitybased quality models break down this complex concept into concrete definitions, more precisely facts about the system, process, and environment as well as their impact on activities performed on and with the system. However, these models lack an operationalisation that would allow them to be used in assessment and prediction of quality. Bayesian networks have been shown to be a viable means for this task incorporating variables with uncertainty. Objective: The qualitative knowledge contained in activity-based quality models are an abundant basis for building Bayesian networks for quality assessment. This paper describes a four-step approach for deriving systematically a Bayesian network from a assessment goal and a quality model. Method: The four steps of the approach are explained in detail and with running examples. Furthermore, an initial evaluation is performed, in which data from NASA projects and an open source system is obtained. The approach is applied to this data and its applicability is analysed. Results: The approach is applicable to the data from the NASA projects and the open source system. However, the predictive results vary depending on the availability and quality of the data, especially the underlying general distributions. Conclusion: The approach is viable in a realistic context but needs further investigation in case studies in order to analyse its predictive validity.

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Section: Resultsmentioning

confidence: 99%

A Bayesian network approach to assess and predict software quality using activity-based quality models

Wagner

2010

Information and Software Technology

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“…Therefore, this observation demonstrates the value of our approach in the aggressive scenario: With the guidance of our approach, developers may avoid a significant percentage of consistency-maintenancerequired cloning operations by not doing a small percentage of cloning operations. Also, the precision of our approach is between 30% and 60%, which is acceptable because the data in our problem is rather imbalanced 13 . Second, the experimental results for the two projects have different warning rate for the same threshold.…”

Section: Effectiveness In the Two Scenariosmentioning

confidence: 88%

Predicting Consistency-Maintenance Requirement of Code Clonesat Copy-and-Paste Time

Wang¹,

Dang

Zhang

et al. 2014

IIEEE Trans. Software Eng.

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Abstract-Code clones have always been a double edged sword in software development. On one hand, it is a very convenient way to reuse existing code, and to save coding effort. On the other hand, since developers may need to ensure consistency among cloned code segments, code clones can lead to extra maintenance effort and even bugs. Recently studies on the evolution of code clones show that only some of the code clones experience consistent changes during their evolution history. Therefore, if we can accurately predict whether a code clone will experience consistent changes, we will be able to provide useful recommendations to developers on leveraging the convenience of some code cloning operations, while avoiding other code cloning operations to reduce future consistency maintenance effort. In this paper, we define a code cloning operation as consistency-maintenance-required if its generated code clones experience consistent changes in the software evolution history, and we propose a novel approach that automatically predicts whether a code cloning operation requires consistency maintenance at the time point of performing copy-and-paste operations. Our insight is that whether a code cloning operation requires consistency maintenance may relate to the characteristics of the code to be cloned and the characteristics of its context. Based on a number of attributes extracted from the cloned code and the context of the code cloning operation, we use Bayesian Networks, a machine-learning technique, to predict whether an intended code cloning operation requires consistency maintenance. We evaluated our approach on four subjects -two large-scale Microsoft software projects, and two popular open-source software projects -under two usage scenarios: 1) recommend developers to perform only the cloning operations predicted to be very likely to be consistency-maintenance-free, and 2) recommend developers to perform all cloning operations unless they are predicted very likely to be consistency-maintenance-required. In the first scenario, our approach is able to recommend developers to perform more than 50% cloning operations with a precision of at least 94% in the four subjects. In the second scenario, our approach is able to avoid 37% to 72% consistency-maintenance-required code clones by warning developers on only 13% to 40% code clones, in the four subjects.

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“…These types of clones are detectable today in an efficient and effective way. Even clones with additional changes (inconsistent, near-miss or type-3 clones) can be detected by several detection approaches and tools (Kamiya, Kusumoto & Inoue, 2002;Deissenboeck et al, 2008;Jiang et al, 2007a). There are also two surveys (Koschke, 2007;Roy & Cordy, 2007) and a systematic literature review (Rattan, Bhatia & Singh, 2013) on this topic.…”

Section: Related Workmentioning

confidence: 99%

“…In the end, we chose two clone detection tools that both can analyse Java and C programs: ConQAT (Deissenboeck et al, 2008) and Deckard (Jiang et al, 2007a). ConQAT is described in Rattan, Bhatia & Singh (2013) as modern, useful and fast open-source clone detector framework.…”

Section: Clone Detection Toolsmentioning

confidence: 99%

How are functionally similar code clones syntactically different? An empirical study and a benchmark

Wagner

Abdulkhaleq

Bogicevic

et al. 2016

PeerJ Computer Science

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Background. Today, redundancy in source code, so-called ''clones'' caused by copy &paste can be found reliably using clone detection tools. Redundancy can arise also independently, however, not caused by copy&paste. At present, it is not clear how only functionally similar clones (FSC) differ from clones created by copy&paste. Our aim is to understand and categorise the syntactical differences in FSCs that distinguish them from copy&paste clones in a way that helps clone detection research. Methods. We conducted an experiment using known functionally similar programs in Java and C from coding contests. We analysed syntactic similarity with traditional detection tools and explored whether concolic clone detection can go beyond syntax. We ran all tools on 2,800 programs and manually categorised the differences in a random sample of 70 program pairs. Results. We found no FSCs where complete files were syntactically similar. We could detect a syntactic similarity in a part of the files in <16% of the program pairs. Concolic detection found 1 of the FSCs. The differences between program pairs were in the categories algorithm, data structure, OO design, I/O and libraries. We selected 58 pairs for an openly accessible benchmark representing these categories. Discussion. The majority of differences between functionally similar clones are beyond the capabilities of current clone detection approaches. Yet, our benchmark can help to drive further clone detection research.

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Tool Support for Continuous Quality Control

Cited by 64 publications

References 8 publications

A Bayesian network approach to assess and predict software quality using activity-based quality models

A Bayesian network approach to assess and predict software quality using activity-based quality models

Predicting Consistency-Maintenance Requirement of Code Clonesat Copy-and-Paste Time

How are functionally similar code clones syntactically different? An empirical study and a benchmark

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