Predicting risk of software changes

Mockus, Audris; Weiss, David M.

doi:10.1002/bltj.2229

Cited by 345 publications

(293 citation statements)

References 30 publications

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“…Such test suites are often created by the programmers who wrote the application and benefit from deep knowledge of the program logic, or by dedicated QA teams which systematically evaluate the main program features and possible corner cases. Furthermore, regression tests often cover program paths that previously triggered bugs, which are more likely to contain further errors [28], [42]. For instance, while the visible symptoms of the offending bugs are fixed, it can happen that the root cause of the bugs is not; alternatively, slightly different executions could still trigger the same bug.…”

Section: Overviewmentioning

confidence: 99%

make test-zesti: A symbolic execution solution for improving regression testing

Marinescu¹,

Cadar²

2012

2012 34th International Conference on Software Engineering (ICSE)

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Abstract-Software testing is an expensive and time consuming process, often involving the manual creation of comprehensive regression test suites. However, current testing methodologies do not take full advantage of these tests. In this paper, we present a technique for amplifying the effect of existing test suites using a lightweight symbolic execution mechanism, which thoroughly checks all sensitive operations (e.g., pointer dereferences) executed by the test suite for errors, and explores additional paths around sensitive operations. We implemented this technique in a prototype system called ZESTI (Zero-Effort Symbolic Test Improvement), and applied it to three open-source code bases-GNU Coreutils, libdwarf and readelf-where it found 52 previously unknown bugs, many of which are out of reach of standard symbolic execution. Our technique works transparently to the tester, requiring no additional human effort or changes to source code or tests.

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Section: Overviewmentioning

confidence: 99%

make test-zesti: A symbolic execution solution for improving regression testing

Marinescu¹,

Cadar²

2012

2012 34th International Conference on Software Engineering (ICSE)

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“…Dynamic instead of static coupling information was used-collected at runtime and from dynamic UML models-to predict changes in terms of lines changed [3]. In [39] and [40], two approaches were presented that focus on the probability that a source code change will introduce a failure.…”

Section: Related Workmentioning

confidence: 99%

Can we predict types of code changes? An empirical analysis

Giger

Pinzger

Gall

2012

2012 9th IEEE Working Conference on Mining Software Repositories (MSR)

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Abstract-There exist many approaches that help in pointing developers to the change-prone parts of a software system. Although beneficial, they mostly fall short in providing details of these changes. Fine-grained source code changes (SCC) capture such detailed code changes and their semantics on the statement level. These SCC can be condition changes, interface modifications, inserts or deletions of methods and attributes, or other kinds of statement changes. In this paper, we explore prediction models for whether a source file will be affected by a certain type of SCC. These predictions are computed on the static source code dependency graph and use social network centrality measures and object-oriented metrics. For that, we use change data of the Eclipse platform and the Azureus 3 project. The results show that Neural Network models can predict categories of SCC types. Furthermore, our models can output a list of the potentially change-prone files ranked according to their change-proneness, overall and per change type category.

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“…al. [37] found that developer experience is essential to predicting failures. In [38], Weyuker et.al.…”

Section: People Related Metrics In Software Defect Predictionmentioning

confidence: 99%

Influence of confirmation biases of developers on software quality: an empirical study

Çalıklı

Bener

2012

Software Qual J

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The thought processes of people have a significant impact on software quality, as software is designed, developed and tested by people. Cognitive biases, which are defined as deviations of human mind from the laws of logic and mathematics, are likely to cause software defects. However, there is little empirical evidence to date to substantiate this assertion. In this research, we focus on a specific cognitive bias type called confirmation bias, which is defined as the tendency of people to seek for evidence to verify hypotheses rather than seeking for evidence to falsify them. Due to confirmation bias, developers might perform unit tests to make their program work rather than to break. Hence, confirmation bias is believed to be one of the factors that lead to increased software defect density. In this research, we present a metric scheme to explore the impact of developers' confirmation bias on software defect density. In order to estimate effectiveness of our metric scheme in quantification of confirmation bias within the context of software development, we performed an empirical study that addressed the prediction of the defective parts of software. In our empirical study, we used confirmation bias metrics on five datasets obtained from two companies. Our results provide empirical evidence that human thought processes and cognitive aspects deserve further investigation to improve decision making in software development for effective process management and resource allocation.

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Predicting risk of software changes

Cited by 345 publications

References 30 publications

make test-zesti: A symbolic execution solution for improving regression testing

make test-zesti: A symbolic execution solution for improving regression testing

Can we predict types of code changes? An empirical analysis

Influence of confirmation biases of developers on software quality: an empirical study

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