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  1. ; ( , Journal of Computer Languages)
    We introduce a representation for generating explanations for the outcomes of combinatorial optimization algorithms. The two key ideas are (A) to maintain fine-grained representations of the values manipulated by these algorithms and (B) to derive explanations from these representations through merge, filter, and aggregation operations. An explanation in our model presents essentially a high-level comparison of the solution to a problem with a hypothesized alternative, illuminating why the solution is better than the alternative. Our value representation results in explanations smaller than other dynamic program representations, such as traces. Based on a measure for the conciseness of explanations we demonstrate through a number of experiments that the explanations produced by our approach are small and scale well with problem size across a number of different applications. 
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    Free, publicly-accessible full text available June 1, 2025
  2. ; ; ( , Journal of Computer Languages)
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  3. ; ( , ACM SIGPLAN Conference on Generative Programming: Concepts & Experiences)
    MADMAX is a Haskell-embedded DSL for multi-attribute, multi-layered decision making. An important feature of this DSL is the ability to generate explanations of why a computed optimal solution is better than its alternatives. The functional approach and Haskell's type system support a high-level formulation of decision-making problems, which facilitates a number of innovations, including the gradual evolution and adaptation of problem representations, a more user-friendly form of sensitivity analysis based on problem domain data, and fine-grained control over explanations. 
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  4. ; ; ; ( , IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC))
    Program traces are often used for explaining the dynamic behavior of programs. Unfortunately, program traces can grow quite big very quickly, even for small programs, which compromises their usefulness. In this paper we present a visual notation for program traces that supports their succinct representation, as well as their dynamic transformation through a structured query language. An evaluation on a set of standard examples shows that our representation can reduce the overall size of traces by more than 80\%, which suggests that our notation is an effective improvement over the use of plain traces in the explanation of dynamic program behavior. 
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  5. ; ; ; ( , Asian Symposium on Programming Languages and Systems)
    Program traces are a sound basis for explaining the dynamic behavior of programs. Alas, program traces can grow big very quickly, even for small programs, which diminishes their value as explanations. In this paper we demonstrate how the systematic simplification of traces can yield succinct program explanations. Specifically, we introduce operations for transforming traces that facilitate the abstraction of details. The operations are the basis of a query language for the definition of trace filters that can adapt and simplify traces in a variety of ways. The generation of traces is governed by a variant of Call-By-Value semantics which specifically supports parsimony in trace representations. We show that our semantics is a conservative extension of Call-By-Value that can produce smaller traces and that the evaluation traces preserve the explanatory content of proof trees at a much smaller footprint. 
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