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

Evolutionary computing driven search based software testing and correction

For a given program, testing, locating the errors identified, and correcting those errors is a critical, yet expensive process. The field of Search Based Software Engineering (SBSE) addresses these phases by formulating them as search problems. This dissertation addresses these challenging problems through the use of two complimentary evolutionary computing based systems. The first one is the Fitness Guided Fault Localization (FGFL) system, which novelly uses a specification based fitness function to perform fault localization. The second is the Coevolutionary Automated Software Correction (CASC) system, which employs a variety of evolutionary computing techniques to perform testing, correction, and verification of software. In support of the real world application of these systems, a practitioner\u27s guide to fitness function design is provided. For the FGFL system, experimental results are presented that demonstrate the applicability of fitness guided fault localization to automate this important phase of software correction in general, and the potential of the FGFL system in particular. For the fitness function design guide, the performance of a guide generated fitness function is compared to that of an expert designed fitness function demonstrating the competitiveness of the guide generated fitness function. For the CASC system, results are presented that demonstrate the system\u27s abilities on a series of problems of both increasing size as well as number of bugs present. The system presented solutions more than 90% of the time for versions of the programs containing one or two bugs. Additionally, scalability results are presented for the CASC system that indicate that success rate linearly decreases with problem size and that the estimated convergence rate scales at worst linearly with problem size --Abstract, page ii