Analogy-Making as a Core Primitive in the Software Engineering Toolbox
This proposes a novel approach to software engineering problems, potentially improving tools for developers, though it appears incremental as it builds on existing cognitive science models.
The paper argues that analogy-making should be a core primitive in software engineering, showing how problems like program understanding and source-code transformation can be reduced to analogy-making, and demonstrates this with Sifter, a new algorithm adapted from Copycat.
An analogy is an identification of structural similarities and correspondences between two objects. Computational models of analogy making have been studied extensively in the field of cognitive science to better understand high-level human cognition. For instance, Melanie Mitchell and Douglas Hofstadter sought to better understand high-level perception by developing the Copycat algorithm for completing analogies between letter sequences. In this paper, we argue that analogy making should be seen as a core primitive in software engineering. We motivate this argument by showing how complex software engineering problems such as program understanding and source-code transformation learning can be reduced to an instance of the analogy-making problem. We demonstrate this idea using Sifter, a new analogy-making algorithm suitable for software engineering applications that adapts and extends ideas from Copycat. In particular, Sifter reduces analogy-making to searching for a sequence of update rule applications. Sifter uses a novel representation for mathematical structures capable of effectively representing the wide variety of information embedded in software. We conclude by listing major areas of future work for Sifter and analogy-making in software engineering.