An integrated search-based approach for automatic testing from extended finite state machine (EFSM) models

This is the post-print version of the Article - Copyright @ 2011 ElsevierThe extended finite state machine (EFSM) is a modelling approach that has been used to represent a wide range of systems. When testing from an EFSM, it is normal to use a test criterion such as transition coverage. Such test criteria are often expressed in terms of transition paths (TPs) through an EFSM. Despite the popularity of EFSMs, testing from an EFSM is difficult for two main reasons: path feasibility and path input sequence generation. The path feasibility problem concerns generating paths that are feasible whereas the path input sequence generation problem is to find an input sequence that can traverse a feasible path. While search-based approaches have been used in test automation, there has been relatively little work that uses them when testing from an EFSM. In this paper, we propose an integrated search-based approach to automate testing from an EFSM. The approach has two phases, the aim of the first phase being to produce a feasible TP (FTP) while the second phase searches for an input sequence to trigger this TP. The first phase uses a Genetic Algorithm whose fitness function is a TP feasibility metric based on dataflow dependence. The second phase uses a Genetic Algorithm whose fitness function is based on a combination of a branch distance function and approach level. Experimental results using five EFSMs found the first phase to be effective in generating FTPs with a success rate of approximately 96.6%. Furthermore, the proposed input sequence generator could trigger all the generated feasible TPs (success rate = 100%). The results derived from the experiment demonstrate that the proposed approach is effective in automating testing from an EFSM

Automatic generation of test sequences form EFSM models using evolutionary algorithms

Automated test data generation through evolutionary testing (ET) is a topic of interest to the software engineering community. While there are many ET-based techniques for automatically generating test data from code, the problem of generating test data from an extended finite state machine (EFSMs) is more complex and has received little attention. In this paper, we introduce a novel approach that addresses the problem of generating input test sequences that trigger given feasible paths in an EFSM model by employing an ET-based technique. The proposed approach expresses the problem as a search for input parameters to be applied to a set of functions to be called sequentially. In order to apply ET-based technique, a new fitness function is introduced to cope with the case when a test target involves calls to a set of transitions sequentially. We evaluate our approach empirically using five sets of randomly generated paths through two EFSM case studies: INRES and class 2 transport protocols. In the experiments, we apply two search techniques: a random and an ET-based which utilizes our new fitness function. Experimental results show that the proposed approach produces input test sequences that trigger all the feasible paths used with a success rate of 100%, however, the random technique failed in most cases with a success rate of 20.8%

A search-based technique for testing from extended finite state machine model

Extended finite state machines (EFSMs), and languages such as state-charts that are similar to EFSMs, are widely used to model state-based systems. When testing from an EFSM M it is common to aim to produce a set of test sequences (input sequences) that satisfies a test criterion that relates to the transition paths (TPs) of M that are executed by the test sequences. For example, we might require that the set of TPs triggered includes all of the transitions of M. One approach to generating such a set of test sequences is to split the problem into two stages: choosing a set of TPs that achieves the test criterion and then producing test sequences to trigger these TPs. However, the EFSM may contain infeasible TPs and the problem of generating a test sequence to trigger a given feasible TP (FTP) is generally uncomputable. In this paper we present a search-based approach that uses two techniques: (1) A TP fitness metric based on our previous work that estimates the feasibility of a given transition path; and (2) A fitness function to guide the search for a test sequence to trigger a given FTP. We evaluated our approach on five EFSMs: A simple in-flight safety system; a class II transport protocol; a lift system; an ATM; and the Inres initiator. In the experiments the proposed approach successfully tested approximately 96.75 % of the transitions and the proposed test sequence generation technique triggered all of the generated FTPs

Search Based Software Engineering in Membrane Computing

This paper presents a testing approach for kernel P Systems (kP systems), based on test data generation for a given scenario. This method uses Genetic Algorithms to generate the input sets needed to trigger the given computation steps

Using formal methods to support testing

Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing

Search-based software engineering: A search-based approach for testing from extended finite state machine (EFSM) models

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The extended finite state machine (EFSM) is a powerful modelling approach that has been applied to represent a wide range of systems. Despite its popularity, testing from an EFSM is a substantial problem for two main reasons: path feasibility and path test case generation. The path feasibility problem concerns generating transition paths through an EFSM that are feasible and satisfy a given test criterion. In an EFSM, guards and assignments in a path‟s transitions may cause some selected paths to be infeasible. The problem of path test case generation is to find a sequence of inputs that can exercise the transitions in a given feasible path. However, the transitions‟ guards and assignments in a given path can impose difficulties when producing such data making the range of acceptable inputs narrowed down to a possibly tiny range. While search-based approaches have proven efficient in automating aspects of testing, these have received little attention when testing from EFSMs. This thesis proposes an integrated search-based approach to automatically test from an EFSM. The proposed approach generates paths through an EFSM that are potentially feasible and satisfy a test criterion. Then, it generates test cases that can exercise the generated feasible paths. The approach is evaluated by being used to test from five EFSM cases studies. The achieved experimental results demonstrate the value of the proposed approach.Aleppo University, Syri

Using genetic algorithms to generate test sequences for complex timed systems

The generation of test data for state based specifications is a computationally expensive process. This problem is magnified if we consider that time con- straints have to be taken into account to govern the transitions of the studied system. The main goal of this paper is to introduce a complete methodology, sup- ported by tools, that addresses this issue by represent- ing the test data generation problem as an optimisa- tion problem. We use heuristics to generate test cases. In order to assess the suitability of our approach we consider two different case studies: a communication protocol and the scientific application BIPS3D. We give details concerning how the test case generation problem can be presented as a search problem and automated. Genetic algorithms (GAs) and random search are used to generate test data and evaluate the approach. GAs outperform random search and seem to scale well as the problem size increases. It is worth to mention that we use a very simple fitness function that can be eas- ily adapted to be used with other evolutionary search techniques

Towards a new framework for TPM compliance testing

Trusted Computing Group (TCG) has proposed the Trusted Computing (TC) concept. Subsequently, TC becomes a common base for many new computing platforms, called Trusted Platform (TP) architecture (hardware and software) that, practically, has a built-in trusted hardware component mounted at the hardware layer and a corresponding trusted software component installed at the operating system level. The trusted hardware component is called Trusted Platform Module (TPM) whose specification has been issued by TCG group and it is implemented by the industry as a tamper- resistant integrated circuit. In practice, the security of an IT TPM-enabled system relies on the correctness of its mounted TPM. Thus, TPM testing is urgently needed to assist in building confidence of the users on the security functionality provided by the TPM. This paper presents the state of the art of the modelling methods being used in the TPM compliance testing. Finally, the paper proposes new framework criteria for TPM Testing that aim at increasing the quality of TPM testing

On Provably Correct Decision-Making for Automated Driving

The introduction of driving automation in road vehicles can potentially reduce road traffic crashes and significantly improve road safety. Automation in road vehicles also brings several other benefits such as the possibility to provide independent mobility for people who cannot and/or should not drive. Many different hardware and software components (e.g. sensing, decision-making, actuation, and control) interact to solve the autonomous driving task. Correctness of such automated driving systems is crucial as incorrect behaviour may have catastrophic consequences. Autonomous vehicles operate in complex and dynamic environments, which requires decision-making and planning at different levels. The aim of such decision-making components in these systems is to make safe decisions at all times. The challenge of safety verification of these systems is crucial for the commercial deployment of full autonomy in vehicles. Testing for safety is expensive, impractical, and can never guarantee the absence of errors. In contrast, formal methods, which are techniques that use rigorous mathematical models to build hardware and software systems can provide a mathematical proof of the correctness of the system. The focus of this thesis is to address some of the challenges in the safety verification of decision-making in automated driving systems. A central question here is how to establish formal verification as an efficient tool for automated driving software development.A key finding is the need for an integrated formal approach to prove correctness and to provide a complete safety argument. This thesis provides insights into how three different formal verification approaches, namely supervisory control theory, model checking, and deductive verification differ in their application to automated driving and identifies the challenges associated with each method. It identifies the need for the introduction of more rigour in the requirement refinement process and presents one possible solution by using a formal model-based safety analysis approach. To address challenges in the manual modelling process, a possible solution by automatically learning formal models directly from code is proposed