- author = "Joseph Renzullo",
- title = "Biological Properties of Software Insights for Measuring and Manipulating Programs",
- school = "Arizona State University",
- year = "2025",
- address = "USA",
- month = dec,
- keywords = "genetic algorithms, genetic programming, genetic improvement, Automated Program Repair, APR, MWRepair, GenProg, MWU, GCC, TensorFlow, SVN, Git, Evolutionary computation, Mutational robustness, Online learning, Software evolution, Computer science, Computer engineering, Automotive engineering, 0540:Automotive engineering, 0984:Computer science, 0464:Computer Engineering, APR-COMP 2024",
- isbn13 = "9798265462671",
- language = "English",
-
URL = "
https://www.proquest.com/dissertations-theses/biological-properties-software-insights-measuring/docview/3279178908/se-2",
- size = "218 pages",
-
abstract = "Automated Program Repair (APR) is a vibrant and
developing field within software engineering that aims
to repair bugs in programs with minimal human
involvement. This is an important problem, because
fixing bugs is the dominant cost of maintaining
software. Progress in APR has been mostly limited to
repairing bugs that exist in one location in the
program, typically requiring only a few lines of code
to be changed. This is not surprising: most APR
algorithms make only one mutation (change to a
program's source code), reverting it if it did not work
before moving on to the next mutation. This caution is
warranted, because it is easier to break a program than
to repair it. However, scaling to more complex repairs
has been a challenge; about half of the bugs in popular
benchmarks have never been repaired by any
algorithm.
This dissertation considers how to modify programs more extensively by focusing on neutral mutations: those that on their own do no harm to the program's behaviour. Remarkably, it is possible to apply many neutral mutations to a program without breaking its behavior. The primary contribution of the dissertation is an algorithm for program repair, MWRepair, that is designed to take advantage of this insight. MWRepair searches for repairs to bugs in programs by identifying many neutral mutations, then combining them in large numbers. This offers the potential of fixing bugs that require modification in more than one location and of fixing bugs that require more complex changes.
There are also a set of secondary contributions of this dissertation. I evaluate the search space for program repair, characterizing the tolerance of a variety of programs across different complexity scales to increasing numbers of neutral mutations. I analyse theoretically and measure empirically the performance of three implementations of the Multiplicative Weights Update algorithm (used in MWRepair). I consider the impact of the age structure of software on the likelihood of productive changes. And I review the trajectory of APR as a field.",
- notes = "Copyright - Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works; Last updated - 2026-02-10",
-
notes = "2025.32286903
Supervisor: Stephanie Forrest",
