A Perspective on Automatic Programming

Most work in automatic programming has focused primarily on the roles of deduction and programming knowledge However, the role played by knowledge of the task domain seems to be at least as important, both for the usability of an automatic programming system and for the feasibility of building one which works on non-trivial problems This perspective has evolved during the course of a variety of studies over the last several years, including detailed examination of existing software for a particular domain (quantitative interpretation of oil well logs) and the implementation of an experimental automatic programming system for that domain The importance of domain knowledge has two important implications: a primary goal of automatic programming research should be to characterize the programming process for specific domains; and a crucial issue to be addressed in these characterizations is the interaction of domain and programming knowledge during program synthesis Used by permission of the International Joint Conferences on Artificial Intelligence; copies of the Proceedings are available from William Kaufmann, Inc , 95 First St., Los Altos, CA 94022 USA. The perspective described here has resulted from the work of many people Steve Smoliar, Stan Vestal, and especially Roger Duffey have been heavily involved in the design, implementation, and retrospective analysis of @o Steve Smoliar and Roger Duffey have done detailed analyses of existing quantitative log interpretation software as well as several hypothetical syntheses Paul Barth has been actively involved in the development of the model of programming for quantitative log interpretation described here; Steve Smoliar and Roger Duffey have contributed several key insights duringrr the process Several of the interpretation developers at SDR have patiently and repeatedly explained the intricacies of log interpretation to us Bruce Buchanan, Randy Davis, Elaine Kant, Tom Mitchell, and Reid Smith provided valuable feedback on earlier drafts of this paper MOST PREVIOUS WORK in automatic programming has focused on the roles played by deduction and programming knowledge in the programming process. For example, the work of Green (1969) and Waldinger and Lee (1969) in the late 1960s was concerned with the use of a theorem-prover to produce programs. This deductive paradigm continues to be the basis for much research in automatic programming (e.g., Manna & Waldinger 1980, Smith 1983). In the mid 1970’s, work on the PSI project (Barstow 1979, Green 1977, Kant 1981) and on the Programmer’s Apprentice (Rich 1981) was fundamentally concerned with the codification of knowledge about programming techniques and the use of that knowledge in program synthesis and analysis Work within the knowledge-based paradigm is also continuing (e.g., Barstow 1982, Waters 1981). This article is concerned with the role played by knowledge of the task domain, a role which seems to be at least as important. One of the reasons for this importance derives from the basic motivating assumption for work on automatic programming: there are many computer users who would prefer not to do their own programming and who would benefit from a facility that could quickly and accurately produce programs for them. The primary concern of these users is not computation they generally are not interested in the idiosyncrasies of the programming process and certainly don’t want to learn the strange notations computer scientists have developed. Rather, they are interested in some application domain they have problems they wish THE AI MAGAZINE Spring 1984 5 AI Magazine Volume 5 Number 1 (1984) (© AAAI)