Automatic type inference is a popular feature of functional programming languages. If a program cannot be typed, the compiler typically reports a single program location in its error message. This location is the point where the type inference failed, but not necessarily the actual source of the error. Other potential error sources are not even considered. Hence, the compiler often misses the true error source, which increases debugging time for the programmer. In this paper, we present a general framework for automatic localization of type errors. Our algorithm finds all minimum error sources, where the exact definition of minimum is given in terms of a compiler-specific ranking criterion. Compilers can use minimum error sources to produce more meaningful error reports, and for automatic error correction. Our approach works by reducing the search for minimum error sources to an optimization problem that we formulate in term of weighted maximum satisfiability modulo theories (MaxSMT). The reduction to weighted MaxSMT allows us to build on SMT solvers to support rich type systems and at the same time abstract from the concrete criterion that is used for ranking the error sources. We have implemented an instance of our framework targeted at Hindley-Milner type systems and evaluated it on existing OCaml benchmarks for type error localization. Our evaluation shows that our approach has the potential to significantly improve the quality of type error reports produced by state-of-the-art compilers.
Compilers for statically typed functional programming languages are notorious for generating confusing type error messages. When the compiler detects a type error, it typically reports the program location where the type checking failed as the source of the error. Since other error sources are not even considered, the actual root cause is often missed. A more adequate approach is to consider all possible error sources and report the most useful one subject to some usefulness criterion. In our previous work, we showed that this approach can be formulated as an optimization problem related to satisfiability modulo theories (SMT). This formulation cleanly separates the heuristic nature of usefulness criteria from the underlying search problem. Unfortunately, algorithms that search for an optimal error source cannot directly use principal types which are crucial for dealing with the exponential-time complexity of the decision problem of polymorphic type checking. In this paper, we present a new algorithm that efficiently finds an optimal error source in a given ill-typed program. Our algorithm uses an improved SMT encoding to cope with the high complexity of polymorphic typing by iteratively expanding the typing constraints from which principal types are derived. The algorithm preserves the clean separation between the heuristics and the actual search. We have implemented our algorithm for OCaml. In our experimental evaluation, we found that the algorithm reduces the running times for optimal type error localization from minutes to seconds and scales better than previous localization algorithms.
This paper studies and optimizes automated program verification. Detailed reasoning about software behavior is often facilitated by program invariants that hold across all program executions. Finding program invariants is in fact an essential step in automated program verification. Automatic discovery of precise invariants, however, can be very difficult in practice. The problem can be simplified if one has access to a candidate set of assertions (or annotations) and the search for invariants is limited over the space defined by these annotations. Then, the main challenge is to automatically generate quality program annotations. We present an approach that infers program annotations automatically by leveraging the history of verifying related programs. Our algorithm extracts high-quality annotations from previous verification attempts, and then applies them for verifying new programs. We present a case study where we applied our algorithm to Microsoft's Static Driver Verifier (SDV). SDV is an industrial-strength tool for verification of Windows device drivers that uses manually-tuned heuristics for obtaining a set of annotations. Our technique inferred program annotations comparable in performance to the existing annotations used in SDV that were devised manually by human experts over years. Additionally, the inferred annotations together with the existing ones improved the performance of SDV overall, proving correct 47% of drivers more while running 22% faster in our experiments.
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