Coverage-based Greybox Fuzzing (CGF) is a random testing approach that requires no program analysis. A new test is generated by slightly mutating a seed input. If the test exercises a new and interesting path, it is added to the set of seeds; otherwise, it is discarded. We observe that most tests exercise the same few "high-frequency" paths and develop strategies to explore significantly more paths with the same number of tests by gravitating towards low-frequency paths. We explain the challenges and opportunities of CGF using a Markov chain model which specifies the probability that fuzzing the seed that exercises path i generates an input that exercises path j. Each state (i.e., seed) has an energy that specifies the number of inputs to be generated from that seed. We show that CGF is considerably more efficient if energy is inversely proportional to the density of the stationary distribution and increases monotonically every time that seed is chosen. Energy is controlled with a power schedule. We implemented the exponential schedule by extending AFL. In 24 hours, AFLFAST exposes 3 previously unreported CVEs that are not exposed by AFL and exposes 6 previously unreported CVEs 7x faster than AFL. AFLFAST produces at least an order of magnitude more unique crashes than AFL.

Since debugging is a time-consuming activity, automated program repair tools such as GenProg have garnered interest. A recent study revealed that the majority of GenProg repairs avoid bugs simply by deleting functionality. We found that SPR, a state-of-the-art repair tool proposed in 2015, still deletes functionality in their many "plausible" repairs. Unlike generate-and-validate systems such as GenProg and SPR, semantic analysis based repair techniques synthesize a repair based on semantic information of the program. While such semantics-based repair methods show promise in terms of quality of generated repairs, their scalability has been a concern so far. In this paper, we present Angelix, a novel semantics-based repair method that scales up to programs of similar size as are handled by search-based repair tools such as GenProg and SPR. This shows that Angelix is more scalable than previously proposed semantics based repair methods such as SemFix and DirectFix. Furthermore, our repair method can repair multiple buggy locations that are dependent on each other. Such repairs are hard to achieve using SPR and GenProg. In our experiments, Angelix generated repairs from large-scale real-world software such as wireshark and php, and these generated repairs include multi-location repairs. We also report our experience in automatically repairing the well-known Heartbleed vulnerability.

Debugging consumes significant time and effort in any major software development project. Moreover, even after the root cause of a bug is identified, fixing the bug is non-trivial. Given this situation, automated program repair methods are of value. In this paper, we present an automated repair method based on symbolic execution, constraint solving and program synthesis. In our approach, the requirement on the repaired code to pass a given set of tests is formulated as a constraint. Such a constraint is then solved by iterating over a layered space of repair expressions, layered by the complexity of the repair code. We compare our method with recently proposed genetic programming based repair on SIR programs with seeded bugs, as well as fragments of GNU Coreutils with real bugs. On these subjects, our approach reports a higher success-rate than genetic programming based repair, and produces a repair faster.

Automated program repair can relieve programmers from the burden of manually fixing the ever-increasing number of programming mistakes.

Researchers have made significant progress in automating the software development process in the past decades. Automated techniques for issue summarization, bug reproduction, fault localization, and program repair have been built to ease the workload of developers. Recent progress in Large Language Models (LLMs) has significantly impacted the development process, where developers can use LLM-based programming assistants to achieve automated coding. Nevertheless, software engineering involves the process of program improvement apart from coding, specifically to enable software maintenance (e.g. program repair to fix bugs) and software evolution (e.g. feature additions). In this paper, we propose an automated approach for solving Github issues to autonomously achieve program improvement. In our approach called AutoCodeRover, LLMs are combined with sophisticated code search capabilities, ultimately leading to a program modification or patch. In contrast to recent LLM agent approaches from AI researchers and practitioners, our outlook is more software engineering oriented. We work on a program representation (abstract syntax tree) as opposed to viewing a software project as a mere collection of files. Our code search exploits the program structure in the form of classes/methods to enhance LLM’s understanding of the issue’s root cause, and effectively retrieve a context via iterative search. The use of spectrum-based fault localization using tests, further sharpens the context, as long as a test-suite is available. Experiments on the recently proposed SWE-bench-lite (300 real-life Github issues) show increased efficacy in solving Github issues (19% on SWE-bench-lite), which is higher than the efficacy of the recently reported Swe-agent. Interestingly, our approach resolved 57 GitHub issues in about 4 minutes each (pass@1), whereas developers spent more than 2.68 days on average. In addition, AutoCodeRover achieved this efficacy with significantly lower cost (on average, $0.43 USD), compared to other baselines. We posit that our workflow enables autonomous software engineering, where, in future, auto-generated code from LLMs can be autonomously improved.