- author = "Andrea Arcuri",
- title = "On the automation of fixing software bugs",
- booktitle = "ICSE Companion '08: Companion of the 30th international conference on Software engineering",
- year = "2008",
- pages = "1003--1006",
- address = "Leipzig, Germany",
- publisher_address = "New York, NY, USA",
- publisher = "ACM",
- note = "Doctoral symposium session",
- keywords = "genetic algorithms, genetic programming, co-evolution, SuA, SBSE",
- isbn13 = "978-1-60558-079-1",
-
URL = "
http://delivery.acm.org/10.1145/1380000/1370223/p1003-arcuri.pdf",
-
DOI = "
doi:10.1145/1370175.1370223",
- size = "4 pages",
- abstract = "Software Testing can take up to half of the resources of the development of new software. Although there has been a lot of work on automating the testing phase, fixing a bug after its presence has been discovered is still a duty of the programmers. Techniques to help the software developers for locating bugs exist though, and they take name of Automated Debugging. However, to our best knowledge, there has been only little attempt in the past to completely automate the actual changing of the software for fixing the bugs. Therefore, in this paper we propose an evolutionary approach to automate the task of fixing bugs. The basic idea is to evolve the programs (e.g., by using Genetic Programming) with a fitness function that is based on how many unit tests they are able to pass. If a formal specification of the buggy software is given, more sophisticated fitness functions can be designed. Moreover, by using the formal specification as an oracle, we can generate as many unit tests as we want. Hence, a co-evolution between programs and unit tests might take place to give even better results. It is important to know that, to fix the bugs in a program with this novel approach, a user needs only to provide either a formal specification or a set of unit tests. No other information is required.",
-
notes = "p1006 {"}We are building our prototype on top of our
previous system for AP{"} \cite{Arcuri:2007:ASE}
also known as \cite{1370223}
Doctoral Symposium of the IEEE International Conference in Software Engineering",
