Residual test coverage monitoring

Pavlopoulou, Christina; Young, Michal

doi:10.1145/302405.302637

Cited by 109 publications

(46 citation statements)

References 13 publications

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“…5. Does the tests selected by our approach have a higher probability of exposing anomalous program behavior than the tests selected by the residual branch coverage approach [33], which is a representative of the existing test selection techniques in practice?…”

Section: Methodsmentioning

confidence: 99%

“…The set S' of unselected tests is automatically generated tests. Residual structural coverage characterizes the structural entities that have not been covered by existing tests [33]. In white-box test augmentation, we select a test that can decrease residual structural coverage and thus increase structural coverage.…”

Section: Unit-test Generation and Selectionmentioning

confidence: 99%

“…In white-box testing (such as testing based on residual structural coverage [33]), programmers can select and inspect the tests that provide new structural coverage unachieved by the existing test suite. Test case prioritization techniques, such as additional structural coverage techniques, can produce a list of sorted tests for regression testing [35,36].…”

Section: Related Workmentioning

confidence: 99%

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Tool-assisted unit-test generation and selection based on operational abstractions

Xie

Notkin

2006

Autom Software Eng

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Abstract. Unit testing, a common step in software development, presents a challenge. When produced manually, unit test suites are often insufficient to identify defects. The main alternative is to use one of a variety of automatic unit-test generation tools: these are able to produce and execute a large number of test inputs that extensively exercise the unit under test. However, without a priori specifications, programmers need to manually verify the outputs of these test executions, which is generally impractical. To reduce this cost, unit-test selection techniques may be used to help select a subset of automatically generated test inputs. Then programmers can verify their outputs, equip them with test oracles, and put them into the existing test suite. In this paper, we present the operational violation approach for unit-test generation and selection, a black-box approach without requiring a priori specifications. The approach dynamically generates operational abstractions from executions of the existing unit test suite. These operational abstractions guide test generation tools to generate tests to violate them. The approach selects those generated tests violating operational abstractions for inspection. These selected tests exercise some new behavior that has not been exercised by the existing tests. We implemented this approach by integrating the use of Daikon (a dynamic invariant detection tool) and Parasoft Jtest (a commercial Java unit testing tool), and conducted several experiments to assess the approach.

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Section: Methodsmentioning

confidence: 99%

Section: Unit-test Generation and Selectionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Tool-assisted unit-test generation and selection based on operational abstractions

Xie

Notkin

2006

Autom Software Eng

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“…In addition to the work of Pavlopulou and Young [2], several systems have been used to remotely monitor aspects of software after its deployment for testing purposes. In fact, most of them focus on low-level aspects such as statement or method coverage, hot-spots identification, error and exception detection, and so on.…”

Section: Related Workmentioning

confidence: 99%

“…Residual testing techniques, as introduced by Pavlopulou and Young [2], allow software developers to cope with this inevitable situation. These strategies test software released either to beta testers or to end-users with respect to its remaining obligations (the residue).…”

Section: Introductionmentioning

confidence: 99%

Distributed expectation-driven residual testing

Naslavsky

2004

"Second International Workshop on Remote Analysis and Measurement of Software Systems (RAMSS 04)" - W15S Workshop - 26th Intern

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Mining hot calling contexts in small space

D’Elia

Demetrescu

Finocchi

2015

Softw. Pract. Exper.

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Summary Calling context trees (CCTs) associate performance metrics with paths through a program's call graph, providing valuable information for program understanding and performance analysis. In real applications, however, CCTs might easily consist of tens of millions of nodes, making them difficult to analyze and also hurting execution times because of poor access locality. For performance analysis, accurately mining only hot calling contexts may be more useful than constructing an entire CCT with millions of uninteresting paths, because the distribution of context frequencies is typically very skewed. In this article, we show how to exploit this property to considerably reduce the CCT size, introducing a novel runtime data structure, called hot CCT (HCCT), in the spectrum of representations for interprocedural control flow. The HCCT includes only hot nodes and their ancestors in a CCT and can be constructed independently from it by using fast, space‐efficient algorithms for mining frequent items in data streams. With this approach, we can distinguish between hot and cold contexts on the fly while obtaining very accurate frequency counts. We show, both theoretically and experimentally, that the HCCT achieves a similar precision as the CCT in a space that is several orders of magnitude smaller and roughly proportional to the number of hot contexts. Our approach can be effectively combined with previous context‐sensitive profiling techniques, as we show for static bursting. We devise an implementation as a plug‐in for the gcc compiler that incurs a slowdown competitive with the gprof call‐graph profiler while collecting finer‐grained profiles. Copyright © 2015 John Wiley & Sons, Ltd.

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Residual test coverage monitoring

Cited by 109 publications

References 13 publications

Tool-assisted unit-test generation and selection based on operational abstractions

Tool-assisted unit-test generation and selection based on operational abstractions

Distributed expectation-driven residual testing

Mining hot calling contexts in small space

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