A choice relation framework for supporting category-partition test case generation

We describe in this paper a choice relation framework for supporting category-partition test case generation. We capture the constraints among various values (or ranges of values) of the parameters and environment conditions identified from the specification, known formally as choices. We express these constraints in terms of relations among choices and combinations of choices, known formally as test frames. We propose a theoretical backbone and techniques for consistency checks and automatic deductions of relations. Based on the theory, algorithms have been developed for generating test frames from the relations. These test frames can then be used as the basis for generating test cases. Our algorithms take into consideration the resource constraints specified by software testers, thus maintaining the effectiveness of the test frames (and hence test cases) generated.published_or_final_versio

Enhancing partition testing through output variation

A major test case generation approach is to divide the input domain into disjoint partitions, from which test cases can be selected. However, we observe that in some traditional approaches to partition testing, the same partition may be associated with different output scenarios. Such an observation implies that the partitioning of the input domain may not be precise enough for effective software fault detection. To solve this problem, partition testing should be fine-tuned to additionally use the information of output scenarios in test case generation, such that these test cases are more fine-grained not only with respect to the input partitions but also from the perspective of output scenarios

On the identification of categories and choices for specification-based test case generation

HKU CS Tech Report TR 2004-02The category-partition method and the classification-tree method help construct test cases from specifications. In both methods, an early step is to identify a set of categories (or classifications) and choices (or classes). This is often performed in an ad hoc manner due to the absence of systematic techniques. In this paper, we report and discuss three empirical studies to investigate the common mistakes made by software testers in such an ad hoc approach. The empirical studies serve three purposes: (a) to make the knowledge of common mistakes known to other testers so that they can avoid repeating the same mistakes, (b) to facilitate researchers and practitioners develop systematic identification techniques, and (c) to provide a means of measuring the effectiveness of newly developed identification techniques. Based on the results of our studies, we also formulate a checklist to help testers detect such mistakes. © 2004 Elsevier B.V. All rights reserved.postprin

Metadata Extraction in Database Testing

The need for an automated testing tool to test the correctness of the database applications is crucial in our current day since databases play an important role in almost all organizations. Also, database’s behavior need to be verified in order to avoid costly errors and false information being extracted from them. The main aim of this paper was to create a component-based tester called DBSoft that tests the correctness of database application systems. The DBSoft toolkit consists of five tools as follows: information collection with the Parser tool, test case generation with the Input Generator tool, test case implementation with the Output Generator tool, test case validation with the Output Validator tool and report generation with the Report Generator tool

Contributions of tester experience and a checklist guideline to the identification of categories and choices for software testing

An early step for most black-box testing methods is to identify a set of categories and choices (or their equivalents) from the specification. The identification is often performed in an ad hoc manner, thus the quality of categories and choices is in doubt. Poorly identified categories and choices will affect the comprehensiveness of test cases. In this paper, we describe several comparative studies using three commercial specifications and discuss the major results. The objectives of our studies are (a) to investigate the differences in the types and amounts of mistakes made between inexperienced and experienced software testers in an ad hoc identification approach and (b) to determine the extent of mistake reduction after discussing the mistakes with the software testers and providing them with an identification checklist. © 2010 The Author(s).published_or_final_versionSpringer Open Choice, 31 May 201

A choice relation framework for supporting category-partition test case generation

We describe in this paper a choice relation framework for supporting category-partition test case generation. We capture the constraints among various values (or ranges of values) of the parameters and environment conditions identified from the specification, known formally as choices. We express these constraints in terms of relations among choices and combinations of choices, known formally as test frames. We propose a theoretical backbone and techniques for consistency checks and automatic deductions of relations. Based on the theory, algorithms have been developed for generating test frames from the relations. These test frames can then be used as the basis for generating test cases. Our algorithms take into consideration the resource constraints specified by software testers, thus maintaining the effectiveness of the test frames (and hence test cases) generated