A Survey on Software Testing Techniques using Genetic Algorithm

The overall aim of the software industry is to ensure delivery of high quality software to the end user. To ensure high quality software, it is required to test software. Testing ensures that software meets user specifications and requirements. However, the field of software testing has a number of underlying issues like effective generation of test cases, prioritisation of test cases etc which need to be tackled. These issues demand on effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of evolutionary algorithms for automatic test generation has been an area of interest for many researchers. Genetic Algorithm (GA) is one such form of evolutionary algorithms. In this research paper, we present a survey of GA approach for addressing the various issues encountered during software testing.Comment: 13 Page

Model based test suite minimization using metaheuristics

Software testing is one of the most widely used methods for quality assurance and fault detection purposes. However, it is one of the most expensive, tedious and time consuming activities in software development life cycle. Code-based and specification-based testing has been going on for almost four decades. Model-based testing (MBT) is a relatively new approach to software testing where the software models as opposed to other artifacts (i.e. source code) are used as primary source of test cases. Models are simplified representation of a software system and are cheaper to execute than the original or deployed system. The main objective of the research presented in this thesis is the development of a framework for improving the efficiency and effectiveness of test suites generated from UML models. It focuses on three activities: transformation of Activity Diagram (AD) model into Colored Petri Net (CPN) model, generation and evaluation of AD based test suite and optimization of AD based test suite. Unified Modeling Language (UML) is a de facto standard for software system analysis and design. UML models can be categorized into structural and behavioral models. AD is a behavioral type of UML model and since major revision in UML version 2.x it has a new Petri Nets like semantics. It has wide application scope including embedded, workflow and web-service systems. For this reason this thesis concentrates on AD models. Informal semantics of UML generally and AD specially is a major challenge in the development of UML based verification and validation tools. One solution to this challenge is transforming a UML model into an executable formal model. In the thesis, a three step transformation methodology is proposed for resolving ambiguities in an AD model and then transforming it into a CPN representation which is a well known formal language with extensive tool support. Test case generation is one of the most critical and labor intensive activities in testing processes. The flow oriented semantic of AD suits modeling both sequential and concurrent systems. The thesis presented a novel technique to generate test cases from AD using a stochastic algorithm. In order to determine if the generated test suite is adequate, two test suite adequacy analysis techniques based on structural coverage and mutation have been proposed. In terms of structural coverage, two separate coverage criteria are also proposed to evaluate the adequacy of the test suite from both perspectives, sequential and concurrent. Mutation analysis is a fault-based technique to determine if the test suite is adequate for detecting particular types of faults. Four categories of mutation operators are defined to seed specific faults into the mutant model. Another focus of thesis is to improve the test suite efficiency without compromising its effectiveness. One way of achieving this is identifying and removing the redundant test cases. It has been shown that the test suite minimization by removing redundant test cases is a combinatorial optimization problem. An evolutionary computation based test suite minimization technique is developed to address the test suite minimization problem and its performance is empirically compared with other well known heuristic algorithms. Additionally, statistical analysis is performed to characterize the fitness landscape of test suite minimization problems. The proposed test suite minimization solution is extended to include multi-objective minimization. As the redundancy is contextual, different criteria and their combination can significantly change the solution test suite. Therefore, the last part of the thesis describes an investigation into multi-objective test suite minimization and optimization algorithms. The proposed framework is demonstrated and evaluated using prototype tools and case study models. Empirical results have shown that the techniques developed within the framework are effective in model based test suite generation and optimizatio

Using Adaptive Agents to Automatically Generate Test Scenarios from the UML Activity Diagrams

Test case generation is one of the most important issues in software testing research and industrial practice. Test scenarios are frequently used to derive test cases for scenario-based software testing. However, the generation of the test scenarios is usually a manual and labor-intensive task. It is desired that test scenarios can be automatically generated. In this paper, we propose an automated approach using adaptive agents to directly generate test scenarios from the UML activity diagrams

Model-Based Testing Approaches using UML Diagrams: A Systematic Literature Review

Software Unit Testing (SUT) is the starting point for Model-Based Testing (MBT), a testing method. The Unified Modeling Language (UML) has become the standard for modelling software in professional and academic settings. There are various uses for the modelling language known as UML. The findings of an SLR on UML-based model-based testing methodologies are presented in this paper. Thirty-five primary articles about six research issues were examined using selection and exclusion criteria. Methods, model class, intermediate format use, and testing methodology are the primary points of examination. The review outcomes identify future research needs and avenues of inquiry

A Common Protocol for Agent-Based Social Simulation

Traditional (i.e. analytical) modelling practices in the social sciences rely on a very well established, although implicit, methodological protocol, both with respect to the way models are presented and to the kinds of analysis that are performed. Unfortunately, computer-simulated models often lack such a reference to an accepted methodological standard. This is one of the main reasons for the scepticism among mainstream social scientists that results in low acceptance of papers with agent-based methodology in the top journals. We identify some methodological pitfalls that, according to us, are common in papers employing agent-based simulations, and propose appropriate solutions. We discuss each issue with reference to a general characterization of dynamic micro models, which encompasses both analytical and simulation models. In the way, we also clarify some confusing terminology. We then propose a three-stage process that could lead to the establishment of methodological standards in social and economic simulations.Agent-Based, Simulations, Methodology, Calibration, Validation, Sensitivity Analysis

Object Constraint Language Based Test Case Optimisation

Software testing, a pivotal phase in the Software Develop- ment Life Cycle (SDLC), ensures the correctness and perfor- mance of the corresponding software system. Model-based testing (MBT) is a method to validate whether the software system or specification satisfies the pre-defined requirements through design models. However, with modern systems ex- panding in complexity, testing has become a labour-intensive and unpredictable process within the SDLC. Therefore, many test case optimisation (TCO) techniques have been proposed to make the testing process more manageable. But these ap- proaches predominantly focus on code-based strategies, leav- ing systems expressed in Object Constraint Language (OCL) underserved. OCL is a part of the Unified Modeling Lan- guage (UML) standard and is a type of declarative language used to describe system specification by pre- and post- con- ditions. Initially, OCL has been proposed as a constraint language to add more details to the UML model, but along- side the development of OCL itself, there are more and more systems whose specifications are expressed in OCL.This thesis aims to systematically investigate the feasibil- ity of applying TCO techniques to the OCL-defined systems, with an emphasis on test case prioritisation (TCP) and test case minimisation (TCM) processes. A systematic literature review for the directly related topic, UML-based test case generation, is conducted in this thesis. Also, we adapted a set of test case optimisation algorithms and compared the performance between these algorithms under the context of OCL. Moreover, we modified one metric for the TCP evalu- ation process, which made the metric more suitable for the MBT and mutation testing environment. Furthermore, we introduce a full set of mutation operators and corresponding classifications to the OCL standard library, offering practical guidance for optimisation processes.The proposed TCO processes are validated and evalu- ated through four real-world systems expressed in OCL with different complexities. The experiment results demonstrate that for the TCM process, the size of the minimised test suite is reduced from 33.33% to 81.8% without losing any fault de- tection ability. For the TCP process, leveraging the modified evaluation metric, the improvements are up to 50%, indicat- ing that the prioritised test suite can detect system defects earlier when compared to the original one. Evaluating based on the considerations of effectiveness, efficiency and stability, we suggest the NSGA-II for the TCM process and the genetic algorithm for the TCP process. When combining TCP and TCM processes, the TCM process consistently increases the efficiency of the TCP process by reducing the search space for the prioritisation process

A Common Protocol for Agent-Based Social Simulation

Traditional (i.e. analytical) modelling practices in the social sciences rely on a very well established, although implicit, methodological protocol, both with respect to the way models are presented and to the kinds of analysis that are performed. Unfortunately, computer-simulated models often lack such a reference to an accepted methodological standard. This is one of the main reasons for the scepticism among mainstream social scientists that results in low acceptance of papers with agent-based methodology in the top journals. We identify some methodological pitfalls that, according to us, are common in papers employing agent-based simulations, and propose appropriate solutions. We discuss each issue with reference to a general characterization of dynamic micro models, which encompasses both analytical and simulation models. In the way, we also clarify some confusing terminology. We then propose a three-stage process that could lead to the establishment of methodological standards in social and economic simulations.Agent-based, simulations, methodology, calibration, validation.

Search-based system architecture development using a holistic modeling approach

This dissertation presents an innovative approach to system architecting where search algorithms are used to explore design trade space for good architecture alternatives. Such an approach is achieved by integrating certain model construction, alternative generation, simulation, and assessment processes into a coherent and automated framework. This framework is facilitated by a holistic modeling approach that combines the capabilities of Object Process Methodology (OPM), Colored Petri Net (CPN), and feature model. The resultant holistic model can not only capture the structural, behavioral, and dynamic aspects of a system, allowing simulation and strong analysis methods to be applied, it can also specify the architectural design space. Both object-oriented analysis and design (OOA/D) and domain engineering were exploited to capture design variables and their domains and define architecture generation operations. A fully realized framework (with genetic algorithms as the search algorithm) was developed. Both the proposed framework and its suggested implementation, including the proposed holistic modeling approach and architecture alternative generation operations, are generic. They are targeted at systems that can be specified using object-oriented or process-oriented paradigm. The broad applicability of the proposed approach is demonstrated on two examples. One is the configuration of reconfigurable manufacturing systems (RMSs) under multi-objective optimization and the other is the architecture design of a manned lunar landing system for the Apollo program. The test results show that the proposed approach can cover a huge number of architecture alternatives and support the assessment of several performance measures. A set of quality results was obtained after running the optimization algorithm following the proposed framework --Abstract, page iii