Abstract semantic diffing of evolving concurrent programs

Bouajjani, Ahmed; Enea, Constantin; Lahiri, Shuvendu K.

doi:10.1007/s10703-018-0322-2

Cited by 3 publications

(21 citation statements)

References 22 publications

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“…In the first paper "Abstract Semantic Diffing of Evolving Concurrent Programs", Boujjani et al [1] put forward a novel methodology for computing the semantic differences between two closely related concurrent programs (that is, subsequent versions of a program). This framework relies on a semantics based on program traces, modelling program action orders and read/write orders.…”

Section: Selected Papersmentioning

confidence: 99%

Foreword to the special issue on the 2017 Static Analysis Symposium

Ranzato

2019

Form Methods Syst Des

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This volume of Formal Methods in System Design (FMSD) features extended and revised versions of a selection of papers presented at the Static Analysis Symposium (SAS) held on 2017 in New York, NY. The series of Static Analysis Symposia serves as a primary venue for the presentation of theoretical, practical, and applicational advances in the area of static analysis of programs and systems. After the conference, the Program Committee selected the best papers of SAS 2017 and invited their authors to submit an extended version. This special issue of FMSD contains the four high-quality articles that were accepted after a careful reviewing process.

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Section: Selected Papersmentioning

confidence: 99%

Foreword to the special issue on the 2017 Static Analysis Symposium

Ranzato

2019

Form Methods Syst Des

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“…Bouajjani et al [11] announce a method for comparing two dependent concurrent programs, in terms of data flow and cross-thread interferences, under an abstraction that identifies any differences in the limit. The dependence between these abstractions regarding a change of the program which indicates an abstraction is following a regression-free concurrency.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of proposed load balancing algorithm with unstable concurrent programs

Jittawiriyanukoon¹

2019

IJEECS

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<span>IoT is the succeeding cohort of the digital computing environment. A swift progression in the IoT deployment and its applications are on the rise. Improving load balancing mechanisms induces healthier performance of the internet based computing as higher number of users can be comfortable. Implementing full services for tasks with unstable concurrency is an uphill process. One of the encounters allied with this administration is the task partition among the applications, regularly referred as concurrent programs. Through load balancing not only resources are equally utilized but also concurrent job’s response time can be promoted. Therefore, in this paper the widely used load balancing algorithms are investigated and yet the proposed algorithm is introduced. Simulation is employed in order to compare the performance metrics such as mean queue length, utilization and throughput between the recommended and existing algorithms. The proposed algorithm confirms the load balancing and outperforms when processing unstable concurrent programs.</span>

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“…However, manually comparing two programs to identify the semantic difference is difficult, and the situation is exacerbated in the presence of thread interactions: changing a single instruction in a thread may have a ripple effect on many instructions in other threads. Although techniques have been proposed to compute the synchronization difference, e.g., by leveraging model checkers [14], they are expensive , , .…”

Section: Introductionmentioning

confidence: 99%

“…We assume the two programs are closely related versions of an evolving software where changes are made to address issues related to thread synchronization as opposed to the sequential computation logic. Therefore, same as in prior works [14,43], we focus on synchronization differences. However, our method is orders-of-magnitude faster because instead of model checking we leverage a polynomial-time declarative program analysis framework which uses a set of Datalog rules to model and reason about thread interactions.…”

Section: Introductionmentioning

confidence: 99%

“…The first one is in checking the feasibility of data-flow edges. The second one is related to the number of data-flow edges used to characterize a difference, also referred to as the rank of an analysis [14]. Although in the worst case, a precise analysis means the rank needs to be as large as the length of the execution, we restrict it to a small number in our method because prior research [12,39] shows that concurrency bugs often can be exposed by executions with a bounded number of context switches.…”

Section: Introductionmentioning

confidence: 99%

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Datalog-based scalable semantic diffing of concurrent programs

Sung

Lahiri

Enea

et al. 2018

Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering

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When an evolving program is modified to address issues related to thread synchronization, there is a need to confirm the change is correct, i.e., it does not introduce unexpected behavior. However, manually comparing two programs to identify the semantic difference is labor intensive and error prone, whereas techniques based on model checking are computationally expensive. To fill the gap, we develop a fast and approximate static analysis for computing synchronization differences of two programs. The method is fast because, instead of relying on heavy-weight model checking techniques, it leverages a polynomial-time Datalog-based program analysis framework to compute differentiating data-flow edges, i.e., edges allowed by one program but not the other. Although approximation is used our method is sufficiently accurate due to careful design of the Datalog inference rules and iterative increase of the required data-flow edges for representing a difference. We have implemented our method and evaluated it on a large number of multithreaded C programs to confirm its ability to produce, often within seconds, the same differences obtained by human; in contrast, prior techniques based on model checking take minutes or even hours and thus can be 10x to 1000x slower. CCS CONCEPTS• Software and its engineering → Software verification and validation;

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Abstract semantic diffing of evolving concurrent programs

Cited by 3 publications

References 22 publications

Foreword to the special issue on the 2017 Static Analysis Symposium

Foreword to the special issue on the 2017 Static Analysis Symposium

Performance evaluation of proposed load balancing algorithm with unstable concurrent programs

Datalog-based scalable semantic diffing of concurrent programs

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