Recent years have seen the development of successful commercial programming tools based on static analysis technologies, which automatically verify intended properties of programs or find tricky bugs that are difficult to detect by testing techniques. One of the key reasons for this success is that these tools use clever strategies for abstracting programs—most details about a given program are abstracted away by these strategies, unless they are predicted to be crucial for proving a given property about the program or detecting a type of program errors of interest. Developing such a strategy is, however, nontrivial, and is currently done by a large amount of manual engineering efforts in most tool projects. Finding a good abstraction strategy automatically or even reducing these manual efforts involved in the development of such a strategy is considered one of the main open challenges in the area of program analysis.

In this talk, I will explain how I tried to address this challenge with colleagues in the past few years. During this time, we worked on parametric program analyses, where parameters for controlling the degree of program abstraction are expressed explicitly in the specification of the analyses. For those analyses, we developed algorithms for searching for a desired parameter value with respect to a given program and a given property, which use ideas from the neighbouring areas of program analysis such as testing, searching and optimisation. In my talk, I will describe the main ideas behind these algorithms without going into technical details. I will focus on intuitions about why and when these algorithms work. I will also talk briefly about a few lessons that I learnt while working on this problem.

This talk is based on the joint work with Mayur Naik, Xin Zhang, Ravi Mangal, Radu Grigore, Ghila Castelnuovo and Mooly Sagiv.