Abstract

Nowadays, data science software plays an increasingly important role in critical decision making in fields ranging from economy and finance to biology and medicine. As we rely more and more on data science for making decisions, we become increasingly vulnerable to programming errors. Errors that do not cause failures can have serious consequences, since they give no indication that something went wrong.

In this talk, we focus on programming errors related to input data usage. Specifically, we propose an abstract interpretation framework to automatically detect unused input data. We systematically derive static analyses for data usage by abstraction of the program operational trace semantics. We propose a new abstract domain to detect single unused input data stored in scalar vari- ables, and we lift this abstraction by building upon an existing domain for the analysis of compound data structures such as array and lists to detect unused chunks of the data.

Finally, we show that existing static analyses for seemingly different problems can be cast into our framework. In particular, we show that a form of live variable analysis and secure information flow analyses can be used for input data usage, with varying degrees of precision.