Automatic Program Repair Using Genetic Programming

Abstract

Software quality is an urgent problem. There are so many bugs in industrial program source code that mature software projects are known to ship with both known and unknown bugs [1], and the number of outstanding defects typically exceeds the resources available to address them [2]. This has become a pressing economic problem whose costs in the United States can be measured in the billions of dollars annually [3].A dominant reason that software defects are so expensive is that fixing them remains a manual process. The process of identifying, triaging, reproducing, and localizing a particular bug, coupled with the task of understanding the underlying error, identifying a set of code changes that address it correctly, and then verifying those changes, costs both time [4] and money, and the cost of repairing a defect can increase by orders of magnitude as development progresses [5]. As a result, many defects, including critical security defects [6], remain unaddressed for long periods of time [7]. Moreover, humans are error-prone, and many human fixes are imperfect, in that they are either incorrect or lead to crashes, hangs, corruption, or security problems [8]. As a result, defect repair has become a major component of software maintenance, which in turn consumes up to 90% of the total lifecycle cost of a given piece of software [9].Although considerable research attention has been paid to supporting various aspects of the manual debugging process [10,11], and also to preempting or dynamically addressing particular classes of vulnerabilities, such as buffer overruns [12,13], there exist virtually no previous automated solutions that address the synthesis of patches for general bugs as they are reported in real-world software.The primary contribution of this dissertation is GenProg, one of the very first automatic solutions designed to help alleviate the manual bug repair burden by automatically and generically patching bugs in deployed and legacy software. GenProg uses a novel genetic programming algorithm, guided by test cases and domain-specific operators, to affect scalable, expressive, and high quality automated repair. We present experimental evidence to substantiate our claims that GenProg can repair multiple types of bugs in multiple types of programs, and that it can repair a large proportion of the bugs that human developers address in practice (that it is expressive); that it scales to real-world system sizes (that it is scalable); and that it produces repairs that are of sufficiently high quality. Over the course of this evaluation, we contribute new benchmark sets of real bugs in real open-source software and novel experimental frameworks for quantitatively evaluating an automated repair technique. We also contribute a novel characterization of i Chapter 0Abstract ii the automated repair search space, and provide analysis both of that space and of the performance and scaling behavior of our technique.General automated software repair was unheard of in 2009. In 2013, it has its own multi-paper s...

Automated program repair using genetic programming and model checking

“…There exist two general classes of techniques to improve the correctness, quality, robustness, and reliability of the software [7]: (1) The techniques that are applied during the software development process; these techniques can only be used for the development of the new systems; (2) the techniques that are applied to the software itself (e.g., testing, debugging, and verification).…”

“…As an example, Microsoft reported that Windows 2000 was released with more than 63,000 known bugs, largely due to insufficient resources [6]. On the other hand, post-deployment finding and repairing of bugs are considerably costly compared to the before-release ones [7]. Moreover, it might result in compromising the private or sensitive user information.…”

“…Some of the existing works are defect-specific [13,15], i.e., they can repair only a certain type of bugs [17]. These works either use a pre-defined approach or template [6], or need formal specifications [14], which are rarely attainable in practice [7].…”

“…Moreover, finding and repairing concurrency bugs remain as an important challenge since it is not addressed in general repair methods as GenProg [22]. Some of the existing works also need runtime monitoring, which leads to run-time overhead [7]. In addition, they may cause to increase the source code size.…”

“…In addition, they may cause to increase the source code size. Note that the quality of the final patch applied to the faulty software is another issue that remains a challenge for the current research [7].…”