The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

The use of metaheuristic search techniques for the automatic generation of test data has been a burgeoning interest for many researchers in recent years. Previous attempts to automate the test generation process have been limited, having been constrained by the size and complexity of software, and the basic fact that in general, test data generation is an undecidable problem. Metaheuristic search techniques oer much promise in regard to these problems. Metaheuristic search techniques are highlevel frameworks, which utilise heuristics to seek solutions for combinatorial problems at a reasonable computational cost. To date, metaheuristic search techniques have been applied to automate test data generation for structural and functional testing; the testing of grey-box properties, for example safety constraints; and also non-functional properties, such as worst-case execution time. This paper surveys some of the work undertaken in this eld, discussing possible new future directions of research for each of its dieren t individual areas.

https://www.inf.utfsm.cl/~mcriff/Tesistas/Testing/sbs-survey.pdf

Search‐based software test data generation: a survey

Regression testing is a testing activity that is performed to provide confidence that changes do not harm the existing behaviour of the software. Test suites tend to grow in size as software evolves, often making it too costly to execute entire test suites. A number of different approaches have been studied to maximize the value of the accrued test suite: minimization, selection and prioritization. Test suite minimization seeks to eliminate redundant test cases in order to reduce the number of tests to run. Test case selection seeks to identify the test cases that are relevant to some set of recent changes. Test case prioritization seeks to order test cases in such a way that early fault detection is maximized. This paper surveys each area of minimization, selection and prioritization technique and discusses open problems and potential directions for future research. Copyright © 2010 John Wiley & Sons, Ltd.

Regression testing minimization, selection and prioritization: a survey

Extensively class tested, this text takes an innovative approach to explaining the process of software testing: it defines testing as the process of applying a few well-defined, general-purpose test criteria to a structure or model of the software. The structure of the text directly reflects the pedagogical approach and incorporates the latest innovations in testing, including techniques to test modern types of software such as OO, web applications, and embedded software.

/pdf/introduction-to-software-testing-454uueymyx.pdf

Introduction to Software Testing

Introduction to Software Testing: List of Figures

Software testing is very labor intensive and expensive and accounts for a significant portion of software system development cost. If the testing process could be automated, the cost of developing software could be significantly reduced. Test data generation in program testing is the process of identifying a set of test data that satisfies a selected testing criterion, such as statement coverage and branch coverage. In this article we present a chaining approach for automated software test data generation which builds on the current theory of execution-oriented test data generation. In the chaining approach, test data are derived based on the actual execution of the program under test. For many programs, the execution of the selected statement may require prior execution of some other statements. The existing methods of test data generation may not efficiently generate test data for these types of programs because they only use control flow information of a program during the search process. The chaining approach uses data dependence analysis to guide the search process, i.e., data dependence analysis automatically identifies statements that affect the execution of the selected statement. The chaining approach uses these statements to form a sequence of statements that is to be executed prior to the execution of the selected statement. The experiments have shown that the chaining approach may significantly improve the chances of finding test data as compared to the existing methods of automated test data generation.

The chaining approach for software test data generation

Model based testing is a system testing technique used to test software systems modeled by formal description languages, e.g., an extended finite state machine (EFSM). System models are frequently changed because of specification changes. Selective test generation techniques are used to test the modified parts of the model. However, the size of regression test suites still may be very large. In this paper, we present a model-based regression testing approach that uses EFSM model dependence analysis to reduce regression test suites. The approach automatically identifies the difference between the original model and the modified model as a set of elementary model modifications. For each elementary modification, regression test reduction strategies are used to reduce the regression test suite based on EFSM dependence analysis. Our initial experience shows that the approach may significantly reduce the size of regression test suites.

Model based regression test reduction using dependence analysis

Test data generation in program testing is the process of identifying a set of test data that satisfies a selected testing criterion, such as, statement coverage or branch coverage. The existing methods of test data generation are limited to unit testing and may not efficiently generate test data for programs with procedures. In this paper we present an approach for automated test data generation for programs with procedures. This approach builds on the current theory of execution-oriented test data generation. In this approach, test data are derived based on the actual execution of the program under test. For many programs, the execution of the selected statement may require prior execution of some other statements that may be part of some procedures. The existing methods use only control flow information of a program during the search process and may not efficiently generate test data for these types of programs because they are not able to identify statements that affect execution of the selected statement. Our approach uses data dependence analysis to guide the process of test data generation. Data dependence analysis automatically identifies statements (or procedures) that affect the execution of the selected statement and this information is used to guide the search process. The initial experiments have shown that this approach may improve the chances of finding test data.

Automated test data generation for programs with procedures

During regression testing, a modified system is retested using the existing test suite. Because the size of the test suite may be very large, testers are interested in detecting faults in the system as early as possible during the retesting process. Test prioritization tries to order test cases for execution so the chances of early detection of faults during retesting are increased. The existing prioritization methods are based on the code of the system. System modeling is a widely used technique to model state-based systems. In this paper, we present methods of test prioritization based on state-based models after changes to the model and the system. The model is executed for the test suite and information about model execution is used to prioritize tests. Execution of the model is inexpensive as compared to execution of the system; therefore the overhead associated with test prioritization is relatively small. In addition, we present an analytical framework for evaluation of test prioritization methods. This framework may reduce the cost of evaluation as compared to the existing evaluation framework that is based on experimentation (observation). We have performed an experimental study in which we compared different test prioritization methods. The results of the experimental study suggest that system models may improve the effectiveness of test prioritization with respect to early fault detection.

Test prioritization using system models

Testing large software systems is very laborious and expensive. Model-based test generation techniques are used to automatically generate tests for large software systems. However, these techniques require manually created system models that are used for test generation. In addition, generated test cases are not associated with individual requirements. In this paper, we present a novel approach of requirement-based test generation. The approach accepts a software specification as a set of individual requirements expressed in textual and SDL formats (a common practice in the industry). From these requirements, system model is automatically created with requirement information mapped to the model. The system model is used to automatically generate test cases related to individual requirements. Several test generation strategies are presented. The approach is extended to requirement-based regression test generation related to changes on the requirement level. Our initial experience shows that this approach may provide significant benefits in terms of reduction in number of test cases and increase in quality of a test suite.

Bogdan Korel

Papers

The chaining approach for software test data generation

Model based regression test reduction using dependence analysis

Automated test data generation for programs with procedures

Test prioritization using system models

Requirement-based automated black-box test generation