Co-evolutionary automatic programming for software development

Andrea Arcuri, Xin Yao

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)
337 Downloads (Pure)

Abstract

Since the 1970s the goal of generating programs in an automatic way (i.e., Automatic Programming) has been sought. A user would just define what he expects from the program (i.e., the requirements), and it should be automatically generated by the computer without the help of any programmer. Unfortunately, this task is much harder than expected. Although transformation methods are usually employed to address this problem, they cannot be employed if the gap between the specification and the actual implementation is too wide. In this paper we introduce a novel conceptual framework for evolving programs from their specification. We use genetic programming to evolve the programs, and at the same time we exploit the specification to co-evolve sets of unit tests. Programs are rewarded by how many tests they do not fail, whereas the unit tests are rewarded by how many programs they make to fail. We present and analyse seven different problems on which this novel technique is successfully applied.
Original languageEnglish
Pages (from-to)412-432
JournalInformation Sciences
Volume259
Early online date4 Jan 2010
DOIs
Publication statusPublished - 20 Feb 2014

Keywords

  • Automatic programming
  • Automatic refinement
  • Co-evolution
  • Software testing
  • Genetic programming

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