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Context

R is a programming language that is used in research for statistical computing and mostly by people without a background in computer science. Almost every value in the R programming language can carry attributes (metadata) like names, dim, or class that are silently attached to values and fundamentally changes how they behave (see the chapter on in Advanced R [2]). A plain numeric vector becomes a matrix just by setting its dim attribute, a class attribute decides which function is actually called when using S3 dispatch (e.g., whether print(x) produces a table or a summary), and a factor is "just" an integer vector with levels. Yet, these attributes are assigned dynamically (e.g., with attr(x, "dim") <- c(2, 3), structure(...), or class(x) <- "my-class"), can be dropped unexpectedly by many operations, and are invisible when just reading the code. For a static analysis this is a problem, as without knowing the class of a value we do not know which method a call dispatches to, and without knowing dim or names we cannot judge whether an access like x[, "score"] is even valid.

Within this work we want to statically infer the attributes of R values, making this hidden metadata explicit and available to further analyses!

Problem

You have an R script together with the statically inferred data and control flow graph, and a general abstract interpretation framework that performs fixpoint iteration to infer code constraints.

The challenges are to

• design abstract domains [1] that represent the attributes a value may carry at a given program point (e.g., its potential classes, its dimension, or the names of its elements),
• track how attributes are created, propagated, modified, and dropped across assignments, function calls, and vector operations (respecting R's ), and
• use the inferred attributes to improve downstream analyses (e.g., resolving S3 method dispatch, detecting invalid accesses, or flagging unintentionally lost attributes).

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 [3], a static analyzer for the R programming language, to infer attributes for values in R using the existing abstract interpretation framework [4]
• Start with a sensible subset of attributes (e.g., class, dim, and names) and model how common functions (attr<-, structure, class<-, dim<-, arithmetic operators, subsetting, ...) affect them
• Use the inference to answer concrete questions, such as which S3 method a call dispatches to [5], and evaluate the precision and performance of your analysis on real-world R scripts

Related Work and Further Reading

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If you want to, you can have a first look at flowR for yourself:

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.

Oliver Gerstl