Systematic techniques for more effective fault localization and program repair

Debugging faulty code is a tedious process that is often quite expensive and can require much manual effort. Developers typically perform debugging in two key steps: (1) fault localization, i.e., identifying the location of faulty line(s) of code; and (2) program repair, i.e., modifying the code to remove the fault(s). Automating debugging to reduce its cost has been the focus of a number of research projects during the last decade, which have introduced a variety of techniques. However, existing techniques suffer from two basic limitations. One, they lack accuracy to handle real programs. Two, they focus on automating only one of the two key steps, thereby leaving the other key step to the developer. Our thesis is that an approach that integrates systematic search based on state-of-the-art constraint solvers with techniques to analyze artifacts that describe application specific properties and behaviors, provides the basis for developing more effective debugging techniques. We focus on faults in programs that operate on structurally complex inputs, such as heap-allocated data or relational databases. Our approach lays the foundation for a unified framework for localization and repair of faults in programs. We embody our thesis in a suite of integrated techniques based on propositional satisfiability solving, correctness specifications analysis, test-spectra analysis, and rule-learning algorithms from machine learning, implement them as a prototype tool-set, and evaluate them using several subject programs.Electrical and Computer Engineerin

Directed random testing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 155-162).Random testing can quickly generate many tests, is easy to implement, scales to large software applications, and reveals software errors. But it tends to generate many tests that are illegal or that exercise the same parts of the code as other tests, thus limiting its effectiveness. Directed random testing is a new approach to test generation that overcomes these limitations, by combining a bottom-up generation of tests with runtime guidance. A directed random test generator takes a collection of operations under test and generates new tests incrementally, by randomly selecting operations to apply and finding arguments from among previously-constructed tests. As soon as it generates a new test, the generator executes it, and the result determines whether the test is redundant, illegal, error-revealing, or useful for generating more tests. The technique outputs failing tests pointing to potential errors that should be corrected, and passing tests that can be used for regression testing. The thesis also contributes auxiliary techniques that post-process the generated tests, including a simplification technique that transforms a, failing test into a smaller one that better isolates the cause of failure, and a branch-directed test generation technique that aims to increase the code coverage achieved by the set of generated tests. Applied to 14 widely-used libraries (including the Java JDK and the core .NET framework libraries), directed random testing quickly reveals many serious, previously unknown errors in the libraries. And compared with other test generation tools (model checking, symbolic execution, and traditional random testing), it reveals more errors and achieves higher code coverage.(cont.) In an industrial case study, a test team at Microsoft using the technique discovered in fifteen hours of human effort as many errors as they typically discover in a person-year of effort using other testing methods.by Carlos Pacheco.Ph.D