FAULT LINKS: IDENTIFYING MODULE AND FAULT TYPES AND THEIR RELATIONSHIP

The presented research resulted in a generic component taxonomy, a generic code-faulttaxonomy, and an approach to tailoring the generic taxonomies into domain-specific aswell as project-specific taxonomies. Also, a means to identify fault links was developed.Fault links represent relationships between the types of code-faults and the types ofcomponents being developed or modified. For example, a fault link has been found toexist between Controller modules (that forms a backbone for any software via. itsdecision making characteristics) and Control/Logic faults (such as unreachable code).The existence of such fault links can be used to guide code reviews, walkthroughs, testingof new code development, as well as code maintenance. It can also be used to direct faultseeding. The results of these methods have been validated. Finally, we also verified theusefulness of the obtained fault links through an experiment conducted using graduatestudents. The results were encouraging

Multi-Point Stride Coverage: A New Genre of Test Coverage Criteria

We introduce a family of coverage criteria, called Multi-Point Stride Coverage (MPSC). MPSC generalizes branch coverage to coverage of tuples of branches taken from the execution sequence of a program. We investigate its potential as a replacement for dataflow coverage, such as def-use coverage. We find that programs can be instrumented for MPSC easily, that the instrumentation usually incurs less overhead than that for def-use coverage, and that MPSC is comparable in usefulness to def-use in predicting test suite effectiveness. We also find that the space required to collect MPSC can be predicted from the number of branches in the program

Formal functional testing of graphical user interfaces.

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Empirical Studies on Automated Software Testing Practices

Software testing is notoriously difficult and expensive, and improper testing carries economic, legal, and even environmental or medical risks. Research in software testing is critical to enabling the development of the robust software that our society relies upon. This dissertation aims to lower the cost of software testing without decreasing the quality by focusing on the use of automation. The dissertation consists of three empirical studies on aspects of software testing. Specifically, these three projects focus on (1) mapping the connections between research topics and the evolution of research topics in the field of software testing, (2) an assessment of the metrics used to guide automated test generation and the factors that suggest when automated test generation can detect real faults, and (3) examination of the semantic coupling between synthetic and real faults in service of improving our ability to cost-effectively generate synthetic faults for use in assessing test case quality. • Project 1 (Mapping): Our main goal for this project is to understand better the emergence of individual research topics and the connection between these topics within the broad field of software testing, enabling the identification of new topics and connections in future research. To achieve this goal, we have applied co-word analysis in order to characterize the topology of software testing research over three decades of research studies based on the keywords provided by the authors of studies indexed in the Scopus database. • Project 2 (Automated Input Generation): We have assessed the fault-detection capabilities of unit test suites generated by automated tools with the goal of satisfying eight fitness functions representing common testing goals. Our purpose was not only to identify the particular fitness functions that detect the most faults but to explore further the factors that influence fault detection. To do this, we gathered observations on the generated test suites and metrics describing the source code of the faulty classes and applied a rule-learning algorithm to identify the factors with the strongest influence on fault detection. • Project 3 (Mutant-Fault Coupling): Synthetic faults (mutants), which can be inserted into code through transformative mutation operators, offer an automated means to assess the effectiveness of test suites and create new test cases. However, mutants can be expensive to utilize and may not realistically model real faults. To enable the cost-effective generation of mutants, we investigate this semantic relationship between mutation operators and real faults

Higher Order Mutation Testing

Mutation testing is a fault-based software testing technique that has been studied widely for over three decades. To date, work in this field has focused largely on first order mutants because it is believed that higher order mutation testing is too computationally expensive to be practical. This thesis argues that some higher order mutants are potentially better able to simulate real world faults and to reveal insights into programming bugs than the restricted class of first order mutants. This thesis proposes a higher order mutation testing paradigm which combines valuable higher order mutants and non-trivial first order mutants together for mutation testing. To overcome the exponential increase in the number of higher order mutants a search process that seeks fit mutants (both first and higher order) from the space of all possible mutants is proposed. A fault-based higher order mutant classification scheme is introduced. Based on different types of fault interactions, this approach classifies higher order mutants into four categories: expected, worsening, fault masking and fault shifting. A search-based approach is then proposed for locating subsuming and strongly subsuming higher order mutants. These mutants are a subset of fault mask and fault shift classes of higher order mutants that are more difficult to kill than their constituent first order mutants. Finally, a hybrid test data generation approach is introduced, which combines the dynamic symbolic execution and search based software testing approaches to generate strongly adequate test data to kill first and higher order mutants

Test data generation : two evolutionary approaches to mutation testing

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