"Computer programming is the process of constructing executable code from fragmentary information. ... When computer programming is done by a machine, the process is called automatic programming. AI researchers are interested in studying automatic programming for two reasons: First, it would be highly useful to have a powerful automatic programming systems that could receive casual and imprecise specifications for a desired target program and then correctly generate that program; second, automatic programming is widely believed to be a necessary component of any intelligent system and is therefore a topic for fundamental research in its own right."
– excerpt from Biermann, A. 1992. Automatic Programming. In Encyclopedia of Artificial Intelligence. 2nd edition, Stuart C. Shapiro, editor, 18 - 35. New York: John Wiley & Sons.
Automated tutoring systems offer the flexibility and scalability necessary to facilitate the provision of high quality and universally accessible programming education. In order to realise the full potential of these systems, recent work has proposed a diverse range of techniques for automatically generating hints to assist students with programming exercises. This paper integrates these apparently disparate approaches into a coherent whole. Specifically, it emphasises that all hint techniques can be understood as a series of simpler components with similar properties. Using this insight, it presents a simple framework for describing such techniques, the Hint Iteration by Narrow-down and Transformation Steps (HINTS) framework, and it surveys recent work in the context of this framework. It discusses important implications of the survey and framework, including the need to further develop evaluation methods and the importance of considering hint technique components when designing, communicating and evaluating hint systems. Ultimately, this paper is designed to facilitate future opportunities for the development, extension and comparison of automated programming hint techniques in order to maximise their educational potential.
The course consists of lectures for the first two-thirds of the semester. Homework problems and programming assignments illustrate the lecture material. The programs are not long; the intent is to gain some exposure to several kinds of programming systems. The latter part of the semester covers readings in the research literature; students are expected to present one or two papers to the class. Many of the world's best researchers in automatic programming are in Austin: Jim Browne, Don Batory, Elaine Kant, Ira Baxter, Ted Biggerstaff; they will be invited to present guest lectures to describe their work.