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Testing Elastic Systems with Surrogate Models
"... Abstract—We combine search-based test case generation and surrogate models for black-box system testing of elastic systems. We aim to efficiently generate tests that expose functional errors and performance problems related to system elasticity. Elastic systems dynamically change their resources all ..."
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Abstract—We combine search-based test case generation and surrogate models for black-box system testing of elastic systems. We aim to efficiently generate tests that expose functional errors and performance problems related to system elasticity. Elastic systems dynamically change their resources allocation to provide consistent quality of service in face of workload fluctuations. However, their ability to adapt could be a double edged sword if not properly designed: They may fail to acquire the right amount of resources or even fail to release them. Blackbox system testing may expose such problems by stimulating system elasticity with suitable sequences of interactions. However, finding such sequences is far from trivial because the number of possible combinations of requests over time is unbounded. In this paper, we analyze the problem of generating test cases for elastic systems, we cast it as a search-based optimization combined with surrogate models, and present the conceptual framework that supports its execution. Index Terms—model based testing, load testing, genetic algorithm, surrogate models I.
Combining Search-based and Adaptive Random Testing Strategies for Environment Model-based Testing of Real-time Embedded Systems
"... Abstract. Effective system testing of real-time embedded systems (RTES) requires a fully automated approach. One such black-box system testing approach is to use environment models to automatically generate test cases and test oracles along with an environment simulator to enable early testing of RT ..."
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Abstract. Effective system testing of real-time embedded systems (RTES) requires a fully automated approach. One such black-box system testing approach is to use environment models to automatically generate test cases and test oracles along with an environment simulator to enable early testing of RTES. In this paper, we propose a hybrid strategy, which combines (1+1) Evolutionary Algorithm (EA) and Adaptive Random Testing (ART), to improve the overall performance of system testing that is obtained when using each single strategy in isolation. An empirical study is carried out on a number of artificial problems and one industrial case study. The novel strategy shows significant overall improvement in terms of fault detection compared to individual performances of both (1+1) EA and ART. 1.
Experiences of Applying UML/MARTE on Three Industrial Projects
"... is a UML profile, which has been developed to model concepts specific to Real-Time and Embedded Systems (RTES). In previous years, we have applied UML/MARTE to three distinct industrial problems in various industry sectors: architecture modeling and configuration of large-scale and highly configurab ..."
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is a UML profile, which has been developed to model concepts specific to Real-Time and Embedded Systems (RTES). In previous years, we have applied UML/MARTE to three distinct industrial problems in various industry sectors: architecture modeling and configuration of large-scale and highly configurable integrated control systems, model-based robustness testing of communication-intensive systems, and model-based environment simulator generation of large-scale RTES for testing. In this paper, we report on our experiences of solving these problems by applying UML/MARTE on four industrial case studies. Based on our common experiences, we derive a framework to help practitioners for future applications of UML/MARTE. The framework provides a set of detailed guidelines on how to apply MARTE in industrial contexts and will help reduce the gap between the modeling standards and industrial needs.
Automating Performance Bottleneck Detection using Search-Based Application Profiling
"... Application profiling is an important performance analysis tech-nique, when an application under test is analyzed dynamically to determine its space and time complexities and the usage of its in-structions. A big and important challenge is to profile nontrivial web applications with large numbers of ..."
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Application profiling is an important performance analysis tech-nique, when an application under test is analyzed dynamically to determine its space and time complexities and the usage of its in-structions. A big and important challenge is to profile nontrivial web applications with large numbers of combinations of their input parameter values. Identifying and understanding particular subset-s of inputs leading to performance bottlenecks is mostly manual, intellectually intensive and laborious procedure. We propose a novel approach for automating performance bottle-neck detection using search-based input-sensitive application profil-ing. Our key idea is to use a genetic algorithm as a search heuristic for obtaining combinations of input parameter values that maxi-mizes a fitness function that represents the elapsed execution time of the application. We implemented our approach, coined as Genetic Algorithm-driven Profiler (GA-Prof) that combines a search-based heuristic with contrast data mining of execution traces to accurate-ly determine performance bottlenecks. We evaluated GA-Prof to determine how effectively and efficiently it can detect injected performance bottlenecks into three popular open source web appli-cations. Our results demonstrate that GA-Prof efficiently explores a large space of input value combinations while automatically and accurately detecting performance bottlenecks, thus suggesting that it is effective for automatic profiling.
Active World Model for Testing Autonomous Systems Using CEFSM
"... Abstract-This paper describes a model-based test generation approach for testing autonomous systems interacting with their environment (i.e., world). Unlike other approaches that assume a static world with attributes and values, we present and test a dynamic world. We use Communicating Extended Fin ..."
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Abstract-This paper describes a model-based test generation approach for testing autonomous systems interacting with their environment (i.e., world). Unlike other approaches that assume a static world with attributes and values, we present and test a dynamic world. We use Communicating Extended Finite State Machine (CEFSM) to illustrate an active world model that describes behaviors of environmental factors (i.e., actors). Abstract World Behavioral Test Cases (AWBTCs) are then generated by covering the active world model using graph coverage criteria. We also generate test-data by input-space partitioning to transform the generated AWBTCs into executable test cases. We apply the World Model-based Test Generation (WMBTG) technique to a case study from the Human-Robot Interaction domain (HRI) specifically a tour-guide robot. Reachability of the active world model and efficiency of coverage criteria are also discussed.
Softw Syst Model DOI 10.1007/s10270-013-0328-6 REGULAR PAPER
"... Environment modeling and simulation for automated testing of soft real-time embedded software ..."
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Environment modeling and simulation for automated testing of soft real-time embedded software
Empirically Evaluating Improved Heuristics for Test Data Generation from OCL Constraints using Search Algorithms
"... Abstract—Efficiently generating test data is one of the key testing requirements of automated model-based test case generation. Keeping this in mind and driven by the needs of our industrial partners, we propose an improvement in heuristics that we previously defined to generate test data from OCL c ..."
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Abstract—Efficiently generating test data is one of the key testing requirements of automated model-based test case generation. Keeping this in mind and driven by the needs of our industrial partners, we propose an improvement in heuristics that we previously defined to generate test data from OCL constraints using search algorithms. We evaluate our improved heuristics using two empirical studies with three