An Automated Framework for Structural Test-data Generation

Structural testing criteria are mandated in many software development standards and guidelines. The process of generating test data to achieve 100% coverage of a given structural coverage metric is labour-intensive and expensive. This paper presents an approach to automate the generation of such test data. The test-data generation is based on the application of a dynamic optimisation-based search for the required test data. The same approach can be generalised to solve other test-data generation problems. Three such applications are discussed-boundary value analysis, assertion/run-time exception testing, and component re-use testing. A prototype tool-set has been developed to facilitate the automatic generation of test data for these structural testing problems. The results of preliminary experiments using this technique and the prototype tool-set are presented and show the efficiency and effectiveness of this approac

Non-linear generation of acoustic noise in the IAR spacecraft

The requirement to produce high level acoustic noise fields with increasing accuracy in environmental test facilities dictates that a more precise understanding is required of the factors controlling nonlinear noise generation. Details are given of various nonlinear effects found in acoustic performance data taken from the IAR Spacecraft Acoustic Chamber. This type of data has enabled the IAR to test large spacecraft to relatively tight acoustic tolerances over a wide frequency range using manually set controls. An analog random noise automatic control system was available and modified to provide automatic selection of the chamber's spectral sound pressure levels. The automatic control system when used to complete a typical qualification test appeared to equal the accuracy of the manual system and had the added advantage that parallel spectra could be easily achieved during preset tests

Towards automatic generation of parameterized test cases from abstractions

Model-based tools for automatic test generation usually can handle systems of a rather limited size. Therefore, they cannot be applied directly to systems of real industrial size. Here, we propose an approach to test generation combining enumerative data abstraction, test generation methods and constraint solving. The approach allows applying enumerative test generation tools like TGV to large and infinite systems. Given such a system, abstractions allow to derive a finite abstract system suitable for automatic test generation with enumerative tools. Abstract test cases need to be parameterized with actual test data, in order to execute them. For data selection, we make use of constraint solving techniques. Test case execution will later be done by TTCN-

A Temporal Logic Based Theory of Test Coverage and Generation

This paper presents a theory of test coverage and generation from specifications written in extended finite state machines (EFSMs). We investigate a family of coverage criteria based on the information of control flow and data flow in EFSMs and characterize them using the temporal logic CTL. We discuss the complexity of minimal cost test generation and describe a simple heuristic which uses the capability of model checkers to construct counterexamples. Our approach extends the range of applications of model checking from automatic verification of finite state systems to automatic test generation from finite state systems

XML-Based Automatic Test Data Generation

Software engineering aims at increasing quality and reliability while decreasing the cost of the software. Testing is one of the most time-consuming phases of the software development lifecycle. Improvement in software testing results in decrease in cost and increase in quality of the software. Automation in software testing is one of the most popular ways of software cost reduction and reliability improvement. In our work we propose a system called XML-based automatic test data generation that generates the test data automatically according to the given data definition. We also proposed a test data definition language to describe the test data to be generated. This system reduces the testing time compared to manual test data generation and increases the testing reliability compared to the random test data generation by eliminating meaningless test data

Test data generation method for dynamic - structural testing in automatic programming assessment

Automatic Programming Assessment or so-called APA has being known as a significant method in assisting lecturers to perform automated assessment and grading on students’ programming assignments. Having to execute a dynamic testing in APA, it is necessary to prepare a set of test data through a systematic test data generation process. Particularly focusing on the software testing research area, various automated methods for test data generation have been proposed. However, they are rarely being utilized in recent studies of APA. There have been limited early attempts to integrate APA and test data generation, but there is still a lack of research in deriving and generating test data for dynamic structural testing. To bridge the gap this study proposes a method of test data generation for dynamic structural testing (or is called DyStruc-TDG). DyStruc-TDG is realized as a tangible deliverable that acts as a test data generator to support APA. The findings from conducted controlled experiment that is based on one-group pre-test and post-test design depict that DyStruc-TDG improves the criteria of reliability (or called positive testing) of test data adequacy in programming assessments. The proposed method is expectantly to assist the lecturers who teach introductory programming courses to derive and generate test data and test cases to perform automatic programming assessment regardless of having a particular knowledge of test cases design in conducting a structural testing. By utilizing this method as part of APA, the lecturers’ workload can be reduced effectively since the typical manual assessments are always prone to errors and leading to inconsistency

Survey on Mutation-based Test Data Generation

The critical activity of testing is the systematic selection of suitable test cases, which be able to reveal highly the faults. Therefore, mutation coverage is an effective criterion for generating test data. Since the test data generation process is very labor intensive, time-consuming and error-prone when done manually, the automation of this process is highly aspired. The researches about automatic test data generation contributed a set of tools, approaches, development and empirical results. In this paper, we will analyse and conduct a comprehensive survey on generating test data based on mutation. The paper also analyses the trends in this field