On Test Design

• Testing is the dominating method for quality assurance of industrial software. Despite its importance and the vast amount of resources invested, there are surprisingly limited efforts spent on testing research, and the few industrially applicable results that emerge are rarely adopted by industry. At the same time, the software industry is in dire need of better support for testing its software within the limited time available. Our aim is to provide a better understanding of how test cases are created and applied, and what factors really impact the quality of the actual test. The plethora of test design techniques (TDTs) available makes decisions on how to test a difficult choice. Which techniques should be chosen and where in the software should they be applied? Are there any particular benefits of using a specific TDT? Which techniques are effective? Which can you automate? What is the most beneficial way to do a systematic test of a system? This thesis attempts to answer some of these questions by providing a set of guidelines for test design, including concrete suggestions for how to improve testing of industrial software systems, thereby contributing to an improved overall system quality. The guidelines are based on ten studies on the understanding and use of TDTs. The studies have been performed in a variety of system domains and consider several different aspects of software test. For example, we have investigated some of the common mistakes in creating test cases that can lead to poor and costly testing. We have also compared the effectiveness of different TDTs for different types of systems. One of the key factors for these comparisons is a profound understanding of faults and their propagation in different systems. Furthermore, we introduce a taxonomy for TDTs based on their effectiveness (fault finding ability), efficiency (fault finding rate), and applicability. Our goal is to provide an improved basis for making well-founded decisions regarding software testing, together with a better understanding of the complex process of test design and test case writing. Our guidelines are expected to lead to improvements in testing of complex industrial software, as well as to higher product quality and shorter time to market.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Software Engineering (hsv//eng)

Nyckelord

Software Test

Test Design Techniques

Test Design

Test Automation

Test Efficiency

Computer science

Datavetenskap

Computer Science

datavetenskap

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