scholarly journals MAP-Coverage: A Novel Coverage Criterion for Testing Thread-Safe Classes

2019 ◽  
Author(s):  
Zan Wang ◽  
Yingquan Zhao ◽  
Shuang Liu ◽  
Jun Sun ◽  
Xiang Chen ◽  
...  
Keyword(s):  
Coverage Criterion

Algorithm for Generating Short Sentences from Grammars Based on Branch Coverage Criterion

2011 ◽  
Vol 22 (11) ◽  
pp. 2564-2576
Author(s):  
Li-Xiao ZHENG ◽  
Zhi-Wu XU ◽  
Hai-Ming CHEN
Keyword(s):  
Branch Coverage ◽  
Coverage Criterion

Parallel Test Prioritization

2022 ◽  
Vol 31 (1) ◽  
pp. 1-50
Author(s):  
Jianyi Zhou ◽  
Junjie Chen ◽  
Dan Hao
Keyword(s):  
Extensive Study ◽  
Good Choice ◽  
Regression Testing ◽  
Sequential Test ◽  
Testing Time ◽  
Parallel Test ◽  
Parallel Testing ◽  
Test Execution ◽  
Coverage Criterion ◽  
Test Prioritization

Although regression testing is important to guarantee the software quality in software evolution, it suffers from the widely known cost problem. To address this problem, existing researchers made dedicated efforts on test prioritization, which optimizes the execution order of tests to detect faults earlier; while practitioners in industry leveraged more computing resources to save the time cost of regression testing. By combining these two orthogonal solutions, in this article, we define the problem of parallel test prioritization, which is to conduct test prioritization in the scenario of parallel test execution to reduce the cost of regression testing. Different from traditional sequential test prioritization, parallel test prioritization aims at generating a set of test sequences, each of which is allocated in an individual computing resource and executed in parallel. In particular, we propose eight parallel test prioritization techniques by adapting the existing four sequential test prioritization techniques, by including and excluding testing time in prioritization. To investigate the performance of the eight parallel test prioritization techniques, we conducted an extensive study on 54 open-source projects and a case study on 16 commercial projects from Baidu , a famous search service provider with 600M monthly active users. According to the two studies, parallel test prioritization does improve the efficiency of regression testing, and cost-aware additional parallel test prioritization technique significantly outperforms the other techniques, indicating that this technique is a good choice for practical parallel testing. Besides, we also investigated the influence of two external factors, the number of computing resources and time allowed for parallel testing, and find that more computing resources indeed improve the performance of parallel test prioritization. In addition, we investigated the influence of two more factors, test granularity and coverage criterion, and find that parallel test prioritization can still accelerate regression testing in parallel scenario. Moreover, we investigated the benefit of parallel test prioritization on the regression testing process of continuous integration, considering both the cumulative acceleration performance and the overhead of prioritization techniques, and the results demonstrate the superiority of parallel test prioritization.

Nonstationary Bayesian kriging: A predictive technique to generate spatial corrections for seismic detection, location, and identification

1998 ◽  
Vol 88 (5) ◽  
pp. 1275-1288 ◽  
Author(s):  
Craig A. Schultz ◽  
Stephen C. Myers ◽  
James Hipp ◽  
Christopher J. Young
Keyword(s):  
Travel Time ◽  
Linear Prediction ◽  
A Priori ◽  
Bayesian Kriging ◽  
Coverage Criterion ◽  
Form Representation ◽  
Seismic Characterization ◽  
Variable Damping ◽  
Data Coverage ◽  
Prediction Techniques

Abstract Seismic characterization works to improve the detection, location, and identification of seismic events by correcting for inaccuracies in geophysical models. These inaccuracies are caused by inherent averaging in the model, and, as a result, exact data values cannot be directly recovered at a point in the model. Seismic characterization involves cataloging reference events so that inaccuracies in the model can be mapped at these points and true data values can be retained through a correction. Application of these corrections to a new event requires the accurate prediction of the correction value at a point that is near but not necessarily coincident with the reference events. Given that these reference events can be sparsely distributed geographically, both interpolation and extrapolation of corrections to the new point are required. In this study, we develop a closed-form representation of Bayesian kriging (linear prediction) that incorporates variable spatial damping. The result is a robust nonstationary algorithm for spatially interpolating geophysical corrections. This algorithm extends local trends when data coverage is good and allows for damping (blending) to an a priori background mean when data coverage is poor. Benchmark tests show that the technique gives reliable predictions of the correction value along with an appropriate uncertainty estimate. Tests with travel-time residual data demonstrate that combining variable damping with an azimuthal coverage criterion reduces the large errors that occur with more classical linear prediction techniques, especially when values are extrapolated in poor coverage regions. In the travel-time correction case, this technique generates both seismic corrections along with uncertainties and can properly incorporate model error in the final location estimate. Results favor the applicability of this nonstationary algorithm to other types of seismic corrections such as amplitude and attenuation measures.

AUTOMATIC PRODUCTION OF TEST DATA WITH A MULTIPLE BATCH-OPTIMISTIC METHOD

2009 ◽  
Vol 18 (01) ◽  
pp. 61-80 ◽  
Author(s):  
ANASTASIS A. SOFOKLEOUS ◽  
ANDREAS S. ANDREOU
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Test Data ◽  
Automatic Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
Generation System ◽  
Automatic Production ◽  
Coverage Criterion ◽  
Testing Coverage

Recent research on software testing focuses on integrating techniques, such as computational intelligence, with special purpose software tools so as to minimize human effort, reduce costs and automate the testing process. This work proposes a complete software testing framework that utilizes a series of specially designed genetic algorithms to generate automatically test data with reference to the edge/condition testing coverage criterion. The framework utilizes a program analyzer, which examines the program's source code and builds dynamically program models for automatic testing, and a test data generation system that utilizes genetic algorithms to search the input space and determine a near to optimum set of test cases with respect to the testing coverage criterion. The performance of the framework is evaluated on a pool of programs consisting of both standard and random-generated programs. Finally, the proposed test data generation system is compared against other similar approaches and the results are discussed.

THE COMPLEXITY OF COVERAGE

2013 ◽  
Vol 24 (02) ◽  
pp. 165-185 ◽  
Author(s):  
KRISHNENDU CHATTERJEE ◽  
LUCA DE ALFARO ◽  
RUPAK MAJUMDAR
Keyword(s):  
Repeated Game ◽  
Test Sequence ◽  
Reactive System ◽  
Memory Requirement ◽  
Coverage Problem ◽  
Deterministic Systems ◽  
Test Input ◽  
Testing Strategies ◽  
Coverage Criterion ◽  
Special Case

We study the problem of generating a test sequence that achieves maximal coverage for a reactive system under test. We formulate the problem as a repeated game between the tester and the system, where the system state space is partitioned according to some coverage criterion and the objective of the tester is to maximize the set of partitions (or coverage goals) visited during the game. We show the complexity of the maximal coverage problem for non-deterministic systems is PSPACE-complete, but is NP-complete for deterministic systems. For the special case of non-deterministic systems with a re-initializing “reset” action, which represent running a new test input on a re-initialized system, we show that the complexity is coNP-complete. Our proof technique for reset games uses randomized testing strategies that circumvent the exponentially large memory requirement of deterministic testing strategies. We also discuss the memory requirement for deterministic strategies and extensions of our results to other models, such as pushdown systems and timed systems.

scholarly journals Simulation-Based Hardware Verification with a Graph-Based Specification

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Zhao Lv ◽  
Shuming Chen ◽  
Yaohua Wang
Keyword(s):  
Pattern Generation ◽  
Ease Of Use ◽  
Hardware Verification ◽  
Verification Method ◽  
Functional Coverage ◽  
Coverage Criterion ◽  
Simulation Based ◽  
Functional Correctness ◽  
Test Vectors ◽  
Verification Techniques

Simulation-based verification continues to be the primary technique for hardware verification due to its scalability and ease of use; however, it lacks exhaustiveness. Although formal verification techniques can exhaustively prove functional correctness, they are limited in terms of the scale of their design due to the state-explosion problem. Alternatively, semiformal approaches can involve a compromise between scalability, exhaustiveness, and resource costs. Therefore, we propose an event-driven flow graph-based specification, which can describe the cycle-accurate functional behaviors without the exploration of whole state space. To efficiently generate input sequences according to the proposed specification, we introduce a functional automatic test pattern generation (ATPG) approach, which involves the proposed intelligent redundancy-reduction strategy to solve problems of random test vectors. We also proposed functional coverage criterion based on the formal specification to support a more reliable measure of verification. We implement a verification platform based on the proposed semiformal approach and compare the proposed semiformal approach with the constrained randomized test (CRT) approach. The experiment results show that the proposed semiformal verification method ensures a more exhaustive and effective exploration of the functional correctness of designs under verification (DUVs).

scholarly journals An Approach to Model Based Testing of Multiagent Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Shafiq Ur Rehman ◽  
Aamer Nadeem
Keyword(s):  
Multiagent Systems ◽  
Autonomous Agents ◽  
Multiagent System ◽  
Dynamic Environment ◽  
Proactive Behavior ◽  
Prototype Tool ◽  
Novel Approach ◽  
Model Based Testing ◽  
Coverage Criterion ◽  
Prometheus Methodology

Autonomous agents perform on behalf of the user to achieve defined goals or objectives. They are situated in dynamic environment and are able to operate autonomously to achieve their goals. In a multiagent system, agents cooperate with each other to achieve a common goal. Testing of multiagent systems is a challenging task due to the autonomous and proactive behavior of agents. However, testing is required to build confidence into the working of a multiagent system. Prometheus methodology is a commonly used approach to design multiagents systems. Systematic and thorough testing of each interaction is necessary. This paper proposes a novel approach to testing of multiagent systems based on Prometheus design artifacts. In the proposed approach, different interactions between the agent and actors are considered to test the multiagent system. These interactions include percepts and actions along with messages between the agents which can be modeled in a protocol diagram. The protocol diagram is converted into a protocol graph, on which different coverage criteria are applied to generate test paths that cover interactions between the agents. A prototype tool has been developed to generate test paths from protocol graph according to the specified coverage criterion.

Model-based testing of software for automation systems using heuristics and coverage criterion

2018 ◽  
Vol 18 (2) ◽  
pp. 797-823
Author(s):  
Rodrigo José Sarmento Peixoto ◽  
Leandro Dias da Silva ◽  
Angelo Perkusich
Keyword(s):  
Model Based ◽  
Automation Systems ◽  
Model Based Testing ◽  
Coverage Criterion
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