Dynamically discovering likely program invariants to support program evolution

Ernst, M.; Cockrell, Jake; Griswold, William G.; Notkin, David

doi:10.1109/32.908957

Cited by 736 publications

(186 citation statements)

References 59 publications

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“…The location of the fault and the cause of the failure are clear after identification and explanation. However, this fault was present and undiscovered in the simulation for several years [Gore 2012].…”

Section: Revisiting the Motivating Example With Elasticmentioning

confidence: 94%

“…Multiple variables within a program can have important relationships that cannot be captured with a single variable scheme. Work on the Daikon project has shown that it is useful to identify and capture the relationships among multiple variables with simple and implicit invariants that aid program evolution and program understanding [Ernst et al 2001]. Similarly, statistical debuggers capture important relationships among multiple variables by identifying invariants that are only violated when the subject program fails.…”

Section: Elastic Predicatesmentioning

confidence: 99%

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Statistical Debugging for Simulations

Gore

Reynolds

Kamensky

et al. 2015

ACM Trans. Model. Comput. Simul.

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Predictions from simulations have entered the mainstream of public policy and decision-making practices. Unfortunately, methods for gaining insight into faulty simulations outputs have not kept pace. Ideally, an insight gathering method would automatically identify the cause of a faulty output and explain to the simulation developer how to correct it. In the field of software engineering, this challenge has been addressed for general-purpose software through statistical debuggers. We present two research contributions, elastic predicates and many-valued labeling functions, that enable debuggers designed for general-purpose software to become more effective for simulations employing random variates and continuous numbers. Elastic predicates address deficiencies of existing debuggers related to continuous numbers, whereas manyvalued labeling functions support the use of random variates. When used in combinations, these contributions allow a simulation developer tasked with localizing the program statement causing the faulty simulation output to examine 40% fewer statements than the leading alternatives. Our evaluation shows that elastic predicates and many-valued labeling functions maintain their ability to reduce the number of program statements that need to be examined under the imperfect conditions that developers experience in practice.

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Section: Revisiting the Motivating Example With Elasticmentioning

confidence: 94%

Section: Elastic Predicatesmentioning

confidence: 99%

Statistical Debugging for Simulations

Gore

Reynolds

Kamensky

et al. 2015

ACM Trans. Model. Comput. Simul.

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show abstract

“…Strategies for computation reuse have already used in other scenarios [19], [20]. The compiler based techniques developed in [19] identify core regions with computations that could be reused during the execution.…”

Section: Related Workmentioning

confidence: 99%

Parallel and Efficient Sensitivity Analysis of Microscopy Image Segmentation Workflows in Hybrid Systems

Barreiros

Teodoro

Kurç

et al. 2017

2017 IEEE International Conference on Cluster Computing (CLUSTER)

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We investigate efficient sensitivity analysis (SA) of algorithms that segment and classify image features in a large dataset of high-resolution images. Algorithm SA is the process of evaluating variations of methods and parameter values to quantify differences in the output. A SA can be very compute demanding because it requires re-processing the input dataset several times with different parameters to assess variations in output. In this work, we introduce strategies to efficiently speed up SA via runtime optimizations targeting distributed hybrid systems and reuse of computations from runs with different parameters. We evaluate our approach using a cancer image analysis workflow on a hybrid cluster with 256 nodes, each with an Intel Phi and a dual socket CPU. The SA attained a parallel efficiency of over 90% on 256 nodes. The cooperative execution using the CPUs and the Phi available in each node with smart task assignment strategies resulted in an additional speedup of about 2×. Finally, multi-level computation reuse lead to an additional speedup of up to 2.46× on the parallel version. The level of performance attained with the proposed optimizations will allow the use of SA in large-scale studies.

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“…The pathway is also vital, owing to its regulation of the growth, proliferation, differentiation and apoptosis (cell-death) of the major cell. The MAPK pathway can also be modelled to capture the in vivo metabolic complexities associated with abnormal cellular growth such as cancer (Ernst et al, 2001). It may also act as a suitable drug target.…”

Section: Mapkmentioning

confidence: 99%

Implementing Mitogen Activated Protein Kinases Cascade on Membrane Computing Using P-Lingua

Shaalan¹,

Muniyandi

2015

Journal of Computer Science

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Abstract:The Mitogen Activated Protein Kinases (MAPK) cascade has been used as a case study for different computational software tools in both modelling and simulation of real-life problems. This study focuses on the development of new techniques for solving a MAPK cascade by using membrane computing. Membrane computing is an unconventional computational approach that provides a platform for modelling discrete system. This approach deals with parallel, distributed and non-deterministic computing models. P-Lingua is used to specify and analyze a wide range of quantitative properties and to offer a general syntactic framework that could be a formal standard for membrane computing. The model is simulated by using MeCoSim, a membrane computing simulator used to verify and validate the model. This study aimed to compare the use of the membrane computing approach for modelling the MAPK cascade with other models. The MAPK cascade, which is specified in P Lingua evaluated based on the simulation with MeCoSim. In general, we can say that the membrane computing model is better than previous models because it takes into account the changes that occur in the cell and because the problem is mainly a problem in the cells; therefore the model achieves the best results and reliability.

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Dynamically discovering likely program invariants to support program evolution

Cited by 736 publications

References 59 publications

Statistical Debugging for Simulations

Statistical Debugging for Simulations

Parallel and Efficient Sensitivity Analysis of Microscopy Image Segmentation Workflows in Hybrid Systems

Implementing Mitogen Activated Protein Kinases Cascade on Membrane Computing Using P-Lingua

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