Demand-driven structural testing with dynamic instrumentation

Misurda, J.; Clause, J.A.; Reed, J.L.; Childers, B.R.; Soffa, M.L.

doi:10.1109/icse.2005.1553558

Cited by 34 publications

(68 citation statements)

References 13 publications

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“…Therefore, a predicate's instrumentation code may be removed from the target once it has triggered [6,31,41]. Dynamic instrumentation removal is especially well suited for branch instrumentation, as each branch predicate adds code on a distinct edge and therefore each branch predicate can be removed independently.…”

Section: Binarization and Dynamic Removalmentioning

confidence: 99%

Cooperative Bug Isolation

2007

Lecture Notes in Computer Science

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Statistical debugging uses lightweight instrumentation and statistical models to identify program behaviors that are strongly predictive of failure. However, most software is mostly correct; nearly all monitored behaviors are poor predictors of failure. We propose an adaptive monitoring strategy that mitigates the overhead associated with monitoring poor failure predictors. We begin by monitoring a small portion of the program, then automatically refine instrumentation over time to zero in on bugs. We formulate this approach as a search on the control-dependence graph of the program. We present and evaluate various heuristics that can be used for this search. We also discuss the construction of a binary instrumentor for incorporating the feedback loop into post-deployment monitoring. Performance measurements show that adaptive bug isolation yields an average performance overhead of 1% for a class of large applications, as opposed to 87% for realistic sampling-based instrumentation and 300% for complete binary instrumentation.

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Section: Binarization and Dynamic Removalmentioning

confidence: 99%

Cooperative Bug Isolation

2007

Lecture Notes in Computer Science

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“…Misurda, et al, [11] described a demand-driven framework for program testing having scalability and flexibility features. It makes use of test paths for the implementation of test coverage.…”

Section: Related Workmentioning

confidence: 99%

An Improved Genetic Algorithm-Based Test Coverage Analysis for Graphical User Interface Software

Adeniyi¹,

Olalekan

2016

AJSEA

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Quality and reliability of software products can be determined through the amount of testing that is carried out on them. One of the metrics that are often employed in measuring the amount of testing is the coverage analysis or adequacy ratio. In the proposed optimized basic Genetic Algorithm (GA) approach, a concept of adaptive mutation was introduced into the basic GA in order for low-fitness chromosomes to have an increased probability of mutation, thereby enhancing their role in the search to produce more efficient search. The main purpose of this concept is to decrease the chance of disrupting a highfitness chromosome and to have the best exploitation of the exploratory role of low-fitness chromosome. The study reveals that the optimized basic GA improves significantly the adequacy ratio or coverage analysis value for Graphical User Interface (GUI) software test over the existing non-adaptive mutation basic GA.

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“…Dynamic compilers are commonly used to speed up interpreted code (e.g., all JVMs are dynamic compilers) and used for on-the-fly program optimizations [2]. Dynamic compilers also have been used to ensure a program does not allow unauthorized control transfers [14], to locate faults by dynamically switching predicates [27], and to perform coverage analysis [18] and impact analysis [4].…”

Section: The Rugrat Testing Processmentioning

confidence: 99%