- author = "Carlos Oliveira and Aldeida Aleti and Yuan-Fang Li and Mohamed Abdelrazek",
- title = "Footprints of fitness functions in search-based software testing",
- booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference",
- year = "2019",
- editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre",
- isbn13 = "978-1-4503-6111-8",
- pages = "1399--1407",
- address = "Prague, Czech Republic",
-
DOI = "
doi:10.1145/3321707.3321880",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- month = "13-17 " # jul,
- organisation = "SIGEVO",
- keywords = "genetic algorithms, genetic programming, SBSE, SBST, Search Based Software Engineering",
-
URL = "
https://users.monash.edu.au/~yli/assets/pdf/pap722s3-file1.pdf",
- size = "9 pages",
-
abstract = "Testing is technically and economically crucial for
ensuring software quality. One of the most challenging
testing tasks is to create test suites that will reveal
potential defects in software. However, as the size and
complexity of software systems increase, the task
becomes more labour-intensive and manual test data
generation becomes infeasible. To address this issue,
researchers have proposed different approaches to
automate the process of generating test data using
search techniques; an area that is known as
Search-Based Software Testing (SBST).
SBST methods require a fitness function to guide the search to promising areas of the solution space. Over the years, a plethora of fitness functions have been proposed. Some methods use control information, others focus on goals. Deciding on what fitness function to use is not easy, as it depends on the software system under test. This work investigates the impact of software features on the effectiveness of different fitness functions. We propose the Mapping the Effectiveness of Test Automation (META) Framework which analyses the footprint of different fitness functions and creates a decision tree that enables the selection of the appropriate function based on software features",
- notes = "Also known as \cite{3321880} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)",
