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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. 

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. 

Abstract In this paper, we present a method for explaining the results produced by dynamic programming (DP) algorithms. Our approach is based on retaining a granular representation of values that are aggregated during program execution. The explanations that are created from the granular representations can answer questions of why one result was obtained instead of another and therefore can increase the confidence in the correctness of program results. Our focus on dynamic programming is motivated by the fact that dynamic programming offers a systematic approach to implementing a large class of optimization algorithms which produce decisions based on aggregated value comparisons. It is those decisions that the granular representation can help explain. Moreover, the fact that dynamic programming can be formalized using semirings supports the creation of a Haskell library for dynamic programming that has two important features. First, it allows programmers to specify programs by recurrence relationships from which efficient implementations are derived automatically. Second, the dynamic programs can be formulated generically (as type classes), which supports the smooth transition from programs that only produce result to programs that can run with granular representation and also produce explanations. Finally, we also demonstrate how to anticipate user questions about program results and how to produce corresponding explanations automatically in advance. 

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. 

Story Programming is an approach for teaching complex computational and algorithmic thinking skills using simple stories anyone can relate to. One could learn these skills independent of a computer or with the use of a computer as a tool to interact with the computation in the tale. This research study examines the use of Story Programming before teaching coding in a computer science orientation course to determine if it is a viable alternative to the code-focused way of teaching the class in the past. We measure the viability of the Story Programming approach by evaluating student-success and learning outcomes, as well as student reactions to post-survey questions. 

Comparative visualizations and the comparison tasks they support constitute a crucial part of visual data analysis on complex data sets. Existing approaches are ad hoc and often require significant effort to produce comparative visualizations, which is impractical especially in cases where visualizations have to be amended in response to changes in the underlying data. We show that the combination of parameterized visualizations and variations yields an effective model for comparative visualizations. Our approach supports data exploration and automatic visualization updates when the underlying data changes. We provide a prototype implementation and demonstrate that our approach covers most of existing comparative visualizations.