A new publication has been accepted at the ICTSS 2017.
Richard Schumi, Priska Lang, Bernhard K. Aichernig, Willibald Krenn, and Rupert Schlick: “Checking Response-Time Properties of Web-Service Applications Under Stochastic User Profiles“, In 29th IFIP International Conference on Testing, Software and Systems (ICTSS 2017), Lecture Notes in Computer Science. Springer, 2017. In press. (PDF)
The paper will be presented at the 29th IFIP International Conference on Testing, Software and Systems (ICTSS 2017) in St-Petersburg, Russia (9-11 October 2017).
Performance evaluation of critical software is important but also computationally expensive. It usually involves sophisticated load-testing tools and demands a large amount of computing resources. Analysing different user populations requires even more effort, becoming infeasible in most realistic cases. Therefore, we propose a model-based approach. We apply model-based test-case generation to generate log-data and learn the associated distributions of response times. These distributions are added to the behavioural models on which we perform statistical model checking (SMC) in order to assess the probabilities of the required response times. Then, we apply classical hypothesis testing to evaluate if an implementation of the behavioural model conforms to these timing requirements. This is the first model-based approach for performance evaluation combining automated test-case generation, cost learning and SMC for real applications. We realised this method with a property-based testing tool, extended with SMC functionality, and evaluate it on an industrial web-service application.
Another publication has been accepted at A-MOST 2017.
Bernhard K. Aichernig, Silvio Marcovic and Richard Schumi: “Property-Based Testing with External Test-Case Generators“, In IEEE 10th International Conference on Software Testing, Verification, and Validation Workshops (ICSTW), 13th Workshop on Advances in Model Based Testing (A-MOST 2017), pages 337–346. IEEE, 2017. (PDF)(doi:10.1109/ICSTW.2017.62)
The paper will be presented on the 13th Workshop on Advances in Model Based Testing A-MOST 2017 in Tokyo, Japan on 17 March 2016. The workshop is part of the 10th IEEE International Conference on Software Testing, Verification and Validation (ICST 2017).
Previous work has demonstrated that property-based testing (PBT) is a flexible random testing technique that facilitates the generation of complex form data. For example, it has been shown that PBT can be applied to web-service applications that require various inputs for web-forms. We want to exploit this data generation feature of PBT and combine it with an external test-case generator that can generate test cases via model-based mutation testing. PBT already supports the generation of test cases from stateful models, but it is limited, because it normally only considers the current state during exploration of the model. We want to give the tester more control on how to produce meaningful operation sequences for test cases. By integrating an external test-case generator into a PBT tool, we can create test cases that follow certain coverage criteria. This allows us to reduce the test execution time, because we do not need a large number of random tests to cover certain model aspects. We demonstrate our approach with a simple example of an external generator for regular expressions and perform an industrial case study, where we integrate an existing model-based mutation testing generator.
A new publication has been accepted at the ICST 2017.
Bernhard K. Aichernig and Richard Schumi: “Statistical Model Checking Meets Property-Based Testing“, In 10th IEEE International Conference on Software Testing, Verification and Validation (ICST 2017), pages 390-400. IEEE, 2017. (PDF)(doi:10.1109/ICST.2017.42)
The paper will be presented at the 10th IEEE International Conference on Software Testing, Verification and Validation (ICST 2017) in Tokyo, Japan (13-17 March 2017).
In recent years, statistical model checking (SMC) has become increasingly popular, because it scales well to larger stochastic models and is relatively simple to implement. SMC solves the model checking problem by simulating the model for finitely many executions and uses hypothesis testing to infer if the samples provide statistical evidence for or against a property. Being based on simulation and statistics, SMC avoids the state-space explosion problem well-known from other model checking algorithms. In this paper we show how SMC can be easily integrated into a property-based testing framework, like FsCheck for C#. As a result we obtain a very flexible testing and simulation environment, where a programmer can define models and properties in a familiar programming language. The advantages: no external modelling language is needed and both stochastic models and implementations can be checked. In addition, we have access to the powerful test-data generators of a property-based testing tool. We demonstrate the feasibility of our approach by repeating three experiments from the SMC literature.