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Register a new userTeaching Design by Contract using Snap!
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Added by
Ra\\'ul E. Monti
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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With the progress in deductive program verification research, new tools and techniques have become available to support design-by-contract reasoning about non-trivial programs written in widely-used programming languages. However, deductive program verification remains an activity for experts, with ample experience in programming, specification and verification. We would like to change this situation, by developing program verification techniques that are available to a larger audience. In this paper, we present how we developed prototypal program verification support for Snap!. Snap! is a visual programming language, aiming in particular at high school students. We added specification language constructs in a similar visual style, designed to make the intended semantics clear from the look and feel of the specification constructs. We provide support both for static and dynamic verification of Snap! programs. Special attention is given to the error messaging, to make this as intuitive as possible.
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The use of behavioural contracts, to specify, regulate and verify systems, is particularly relevant to runtime monitoring of distributed systems. System distribution poses major challenges to contract monitoring, from monitoring-induced information leaks to computation load balancing, communication overheads and fault-tolerance. We present mDPi, a location-aware process calculus, for reasoning about monitoring of distributed systems. We define a family of Labelled Transition Systems for this calculus, which allow formal reasoning about different monitoring strategies at different levels of abstractions. We also illustrate the expressivity of the calculus by showing how contracts in a simple contract language can be synthesised into different mDPi monitors.
Programming by Example (PBE) is a program synthesis paradigm in which the synthesizer creates a program that matches a set of given examples. In many applications of such synthesis (e.g., program repair or reverse engineering), we are to reconstruct a program that is close to a specific target program, not merely to produce some program that satisfies the seen examples. In such settings, we wish that the synthesized program generalizes well, i.e., has as few errors as possible on the unobserved examples capturing the target function behavior. In this paper, we propose the first framework (called SynGuar) for PBE synthesizers that guarantees to achieve low generalization error with high probability. Our main contribution is a procedure to dynamically calculate how many additional examples suffice to theoretically guarantee generalization. We show how our techniques can be used in 2 well-known synthesis approaches: PROSE and STUN (synthesis through unification), for common string-manipulation program benchmarks. We find that often a few hundred examples suffice to provably bound generalization error below $5%$ with high ($geq 98%$) probability on these benchmarks. Further, we confirm this empirically: SynGuar significantly improves the accuracy of existing synthesizers in generating the right target programs. But with fewer examples chosen arbitrarily, the same baseline synthesizers (without SynGuar) overfit and lose accuracy.
LaTeX is a widely-used document preparation system. Its powerful ability in mathematical equation editing is perhaps the main reason for its popularity in academia. Sometimes, however, even an expert user may spend much time on fixing an erroneous equation. In this paper, we present EqFix, a synthesis-based repairing system for LaTeX equations. It employs a set of fixing rules, and can suggest possible repairs for common errors in LaTeX equations. A domain specific language is proposed for formally expressing the fixing rules. The fixing rules can be automatically synthesized from a set of input-output examples. An extension of relaxer is also introduced to enhance the practicality of EqFix. We evaluate EqFix on real-world examples and find that it can synthesize rules with high generalization ability. Compared with a state-of-the-art string transformation synthesizer, EqFix solved 37% more cases and spent only one third of their synthesis time.
354 -
Massimo Bartoletti
, Roberto Zunino (Dipartimento din Ingegneria e Scienza dellInformazione
2010
We investigate how contracts can be used to regulate the interaction between processes. To do that, we study a variant of the concurrent constraints calculus presented in [1], featuring primitives for multi-party synchronization via contracts. We proceed in two directions. First, we exploit our primitives to model some contract-based interactions. Then, we discuss how several models for concurrency can be expressed through our primitives. In particular, we encode the pi-calculus and graph rewriting.
Since regular expressions (abbrev. regexes) are difficult to understand and compose, automatically generating regexes has been an important research problem. This paper introduces TransRegex, for automatically constructing regexes from both natural language descriptions and examples. To the best of our knowledge, TransRegex is the first to treat the NLP-and-example-based regex synthesis problem as the problem of NLP-based synthesis with regex repair. For this purpose, we present novel algorithms for both NLP-based synthesis and regex repair. We evaluate TransRegex with ten relevant state-of-the-art tools on three publicly available datasets. The evaluation results demonstrate that the accuracy of our TransRegex is 17.4%, 35.8% and 38.9% higher than that of NLP-based approaches on the three datasets, respectively. Furthermore, TransRegex can achieve higher accuracy than the state-of-the-art multi-modal techniques with 10% to 30% higher accuracy on all three datasets. The evaluation results also indicate TransRegex utilizing natural language and examples in a more effective way.
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