Context

Let's suppose you are a data scientist tasked with the analysis of a dataset. As an expert of the domain you have a quick look at the dataset and remember an older script by a colleague which already loads, prepares, and transforms the dataset as you want! Reusing it just leaves you with the task of visualizing the data (as is the common workflow) so you quickly write up and run the script... and luckily realize that even though the script runs and produces figures eerily close to what you would expect, something is not right. The dataset of your colleague never contained a zero so the script contains the implicit assumption of being just able to divide cells.
 

Within this work we want to make such implicit assumptions explicit in the code, alerting users whenever they no longer hold!

Problem

You have an R script together with the statically inferred dataflow graph that informs you about the control and data dependencies of function calls, variables, and definitions in the program.

The challenges are to

• identify points of interest at which the behavior of the program is defined,
• infer contracts that represent the potential implicit assumptions at the given position (e.g., that the value of a variable has to be non-zero, smaller than the length of another vector, ...), and
• instrument the code to automatically verify these constraints from now on.

Of course, the specific scope of these challenges as well as the focus depends on whether you want to do this as a bachelor's or master's thesis as well as your personal preference.

Tasks

• Enrich flowR [4],  a dataflow analysis framework for the R programming language, with the capability to infer predefined constraints
• Create an initial collection of sensible constraints to infer (e.g., non-zero values, ...)
• Infer these constraints and instrument the program to reflect them [5]

One way to infer such constraints would be the definition of abstract domains [1] although classical optimization techniques such as constant folding and constant propagation help as well [2, 3].

Related Work and Further Reading

1. P. Cousot. Principles of Abstract Interpretation. (ISBN: 978-0-26-204490-5)
2. K. Cooper and L Torczon. Engineering a Compiler. (ISBN: 978-0-12-818926-9)
3. U. Khedker, A. Sanyal, and B. Sathe. Data Flow Analysis: Theory and Practice. (ISBN: 978-0-8493-3251-7)
4. F. Sihler. Constructing a Static Program Slicer for R Programs.
5. B. Meyer, Applying "Design by Contract"

If you want to, you can have a first look at flowR for yourself: https://github.com/flowr-analysis/flowr.

Contact and More

If you are interested and/or have any questions, feel free to contact me any time.
We can discuss the topic further and try to adapt it to your personal preferences.

Florian Sihler (Institute Homepage)