Finding The Lazy Programmer's Bugs

Traditionally developers and testers created huge numbers of explicit tests, enumerating interesting cases, perhaps biased by what they believe to be the current boundary conditions of the function being tested. Or at least, they were supposed to. A major step forward was the development of property testing. Property testing requires the user to write a few functional properties that are used to generate tests, and requires an external library or tool to create test data for the tests. As such many thousands of tests can be created for a single property. For the purely functional programming language Haskell there are several such libraries; for example QuickCheck [CH00], SmallCheck and Lazy SmallCheck [RNL08]. Unfortunately, property testing still requires the user to write explicit tests. Fortunately, we note there are already many implicit tests present in programs. Developers may throw assertion errors, or the compiler may silently insert runtime exceptions for incomplete pattern matches. We attempt to automate the testing process using these implicit tests. Our contributions are in four main areas: (1) We have developed algorithms to automatically infer appropriate constructors and functions needed to generate test data without requiring additional programmer work or annotations. (2) To combine the constructors and functions into test expressions we take advantage of Haskell's lazy evaluation semantics by applying the techniques of needed narrowing and lazy instantiation to guide generation. (3) We keep the type of test data at its most general, in order to prevent committing too early to monomorphic types that cause needless wasted tests. (4) We have developed novel ways of creating Haskell case expressions to inspect elements inside returned data structures, in order to discover exceptions that may be hidden by laziness, and to make our test data generation algorithm more expressive. In order to validate our claims, we have implemented these techniques in Irulan, a fully automatic tool for generating systematic black-box unit tests for Haskell library code. We have designed Irulan to generate high coverage test suites and detect common programming errors in the process

Program Synthesis With Types

Program synthesis, the automatic generation of programs from specification, promises to fundamentally change the way that we build software. By using synthesis tools, we can greatly speed up the time it takes to build complex software artifacts as well as construct programs that are automatically correct by virtue of the synthesis process. Studied since the 70s, researchers have applied techniques from many different sub-fields of computer science to solve the program synthesis problem in a variety of domains and contexts. However, one domain that has been less explored than others is the domain of typed, functional programs. This is unfortunate because programs in richly-typed languages like OCaml and Haskell are known for ``writing themselves\u27\u27 once the programmer gets the types correct. In light of this observation, can we use type theory to build more expressive and efficient type-directed synthesis systems for this domain of programs? This dissertation answers this question in the affirmative by building novel type-theoretic foundations for program synthesis. By using type theory as the basis of study for program synthesis, we are able to build core synthesis calculi for typed, functional programs, analyze the calculi\u27s meta-theoretic properties, and extend these calculi to handle increasingly richer types and language features. In addition to these foundations, we also present an implementation of these synthesis systems, Myth, that demonstrates the effectiveness of program synthesis with types on real-world code

Practical Type Inference for the GADT Type System

Generalized algebraic data types (GADTs) are a type system extension to algebraic data types that allows the type of an algebraic data value to vary with its shape. The GADT type system allows programmers to express detailed program properties as types (for example, that a function should return a list of the same length as its input), and a general-purpose type checker will automatically check those properties at compile time. Type inference for the GADT type system and the properties of the type system are both currently areas of active research. In this dissertation, I attack both problems simultaneously by exploiting the symbiosis between type system research and type inference research. Deficiencies of GADT type inference algorithms motivate research on specific aspects of the type system, and discoveries about the type system bring in new insights that lead to improved GADT type inference algorithms. The technical contributions of this dissertation are therefore twofold: in addition to new GADT type system properties (such as the prevalence of pointwise type information flow in GADT patterns, a generalized notion of existential types, and the effects of enforcing the GADT branch reachability requirement), I will also present a new GADT type inference algorithm that is significantly more powerful than existing algorithms. These contributions should help programmers use the GADT type system more effectively, and they should also enable language implementers to provide better support for the GADT type system

Reactive machine control : a simulation approach using chi

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New developments to Skalpel : a type error slicing method for explaining errors in type and effect systems

Type error reports provide programmers with a description of type errors which exist in their code. Such descriptions are frequently of poor quality, as they often present just one point in the program, rather than all locations in the code which contribute to that type error. Skalpel is a type error report system for the Standard ML language which tackles this problem, by presenting all and only the locations in the program which contribute to the type error. While the original Skalpel gives substantially better error reports than comparable systems, it has a number of limitations such as a lack of support for language features and poor efficiency. In this research we have made a number of contributions, including a full critique of both the Skalpel core theoretical system and its extensions, support for the remaining features of Standard ML, an analysis and improvements to the efficiency, and an investigation for the first time on Skalpel’s theoretical properties