arXiv subjects

Carlo A. Furia

Publications and source records attributed to Carlo A. Furia.

At least 37 records · Page 2Linked to original sources

arXiv2021

VerifyThis 2019: A Program Verification Competition (Extended Report)

VerifyThis is a series of program verification competitions that emphasize the human aspect: participants tackle the verification of detailed behavioral properties -- something that lies beyond the capabilities of fully automatic verification, and requires instead human expertise to suitably encode programs, specifications, and invariants. This paper describes the 8th edition of VerifyThis, which took place at ETAPS 2019 in Prague. Thirteen teams entered the competition, which consisted of three verification challenges and spanned two days of work. The report analyzes how the participating teams fared on these challenges, reflects on what makes a verification challenge more or less suitable for the typical VerifyThis participants, and outlines the difficulties of comparing the work of teams using wildly different verification approaches in a competition focused on the human aspect.

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arXiv2020

A Method to Assess and Argue for Practical Significance in Software Engineering

A key goal of empirical research in software engineering is to assess practical significance, which answers whether the observed effects of some compared treatments show a relevant difference in practice in realistic scenarios. Even though plenty of standard techniques exist to assess statistical significance, connecting it to practical significance is not straightforward or routinely done; indeed, only a few empirical studies in software engineering assess practical significance in a principled and systematic way. In this paper, we argue that Bayesian data analysis provides suitable tools to assess practical significance rigorously. We demonstrate our claims in a case study comparing different test techniques. The case study's data was previously analyzed (Afzal et al., 2015) using standard techniques focusing on statistical significance. Here, we build a multilevel model of the same data, which we fit and validate using Bayesian techniques. Our method is to apply cumulative prospect theory on top of the statistical model to quantitatively connect our statistical analysis output to a practically meaningful context. This is then the basis both for assessing and arguing for practical significance. Our study demonstrates that Bayesian analysis provides a technically rigorous yet practical framework for empirical software engineering. A substantial side effect is that any uncertainty in the underlying data will be propagated through the statistical model, and its effects on practical significance are made clear. Thus, in combination with cumulative prospect theory, Bayesian analysis supports seamlessly assessing practical significance in an empirical software engineering context, thus potentially clarifying and extending the relevance of research for practitioners.

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arXiv2020

RESTORE: Retrospective Fault Localization Enhancing Automated Program Repair

Fault localization is a crucial step of automated program repair, because accurately identifying program locations that are most closely implicated with a fault greatly affects the effectiveness of the patching process. An ideal fault localization technique would provide precise information while requiring moderate computational resources---to best support an efficient search for correct fixes. In contrast, most automated program repair tools use standard fault localization techniques---which are not tightly integrated with the overall program repair process, and hence deliver only subpar efficiency. In this paper, we present retrospective fault localization: a novel fault localization technique geared to the requirements of automated program repair. A key idea of retrospective fault localization is to reuse the outcome of failed patch validation to support mutation-based dynamic analysis---providing accurate fault localization information without incurring onerous computational costs. We implemented retrospective fault localization in a tool called RESTORE---based on the JAID Java program repair system. Experiments involving faults from the Defects4J standard benchmark indicate that retrospective fault localization can boost automated program repair: RESTORE efficiently explores a large fix space, delivering state-of-the-art effectiveness (41 Defects4J bugs correctly fixed, 8 more than any other automated repair tools for Java) while simultaneously boosting performance (speedup over 3 compared to JAID). Retrospective fault localization is applicable to any automated program repair techniques that rely on fault localization and dynamic validation of patches.

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arXiv2020

Bayesian data analysis in empirical software engineering---The case of missing data

Bayesian data analysis (BDA) is today used by a multitude of research disciplines. These disciplines use BDA as a way to embrace uncertainty by using multilevel models and making use of all available information at hand. In this chapter, we first introduce the reader to BDA and then provide an example from empirical software engineering, where we also deal with a common issue in our field, i.e., missing data. The example we make use of presents the steps done when conducting state of the art statistical analysis. First, we need to understand the problem we want to solve. Second, we conduct causal analysis. Third, we analyze non-identifiability. Fourth, we conduct missing data analysis. Finally, we do a sensitivity analysis of priors. All this before we design our statistical model. Once we have a model, we present several diagnostics one can use to conduct sanity checks. We hope that through these examples, the reader will see the advantages of using BDA. This way, we hope Bayesian statistics will become more prevalent in our field, thus partly avoiding the reproducibility crisis we have seen in other disciplines.

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arXiv2019

Bayesian Data Analysis in Empirical Software Engineering Research

Statistics comes in two main flavors: frequentist and Bayesian. For historical and technical reasons, frequentist statistics have traditionally dominated empirical data analysis, and certainly remain prevalent in empirical software engineering. This situation is unfortunate because frequentist statistics suffer from a number of shortcomings---such as lack of flexibility and results that are unintuitive and hard to interpret---that curtail their effectiveness when dealing with the heterogeneous data that is increasingly available for empirical analysis of software engineering practice. In this paper, we pinpoint these shortcomings, and present Bayesian data analysis techniques that provide tangible benefits---as they can provide clearer results that are simultaneously robust and nuanced. After a short, high-level introduction to the basic tools of Bayesian statistics, we present the reanalysis of two empirical studies on the effectiveness of automatically generated tests and the performance of programming languages. By contrasting the original frequentist analyses with our new Bayesian analyses, we demonstrate the concrete advantages of the latter. To conclude we advocate a more prominent role for Bayesian statistical techniques in empirical software engineering research and practice.

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arXiv2019

Evolution of statistical analysis in empirical software engineering research: Current state and steps forward

Software engineering research is evolving and papers are increasingly based on empirical data from a multitude of sources, using statistical tests to determine if and to what degree empirical evidence supports their hypotheses. To investigate the practices and trends of statistical analysis in empirical software engineering (ESE), this paper presents a review of a large pool of papers from top-ranked software engineering journals. First, we manually reviewed 161 papers and in the second phase of our method, we conducted a more extensive semi-automatic classification of papers spanning the years 2001--2015 and 5,196 papers. Results from both review steps was used to: i) identify and analyze the predominant practices in ESE (e.g., using t-test or ANOVA), as well as relevant trends in usage of specific statistical methods (e.g., nonparametric tests and effect size measures) and, ii) develop a conceptual model for a statistical analysis workflow with suggestions on how to apply different statistical methods as well as guidelines to avoid pitfalls. Lastly, we confirm existing claims that current ESE practices lack a standard to report practical significance of results. We illustrate how practical significance can be discussed in terms of both the statistical analysis and in the practitioner's context.

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arXiv2018

Robustness Testing of Intermediate Verifiers

Program verifiers are not exempt from the bugs that affect nearly every piece of software. In addition, they often exhibit brittle behavior: their performance changes considerably with details of how the input program is expressed-details that should be irrelevant, such as the order of independent declarations. Such a lack of robustness frustrates users who have to spend considerable time figuring out a tool's idiosyncrasies before they can use it effectively. This paper introduces a technique to detect lack of robustness of program verifiers; the technique is lightweight and fully automated, as it is based on testing methods (such as mutation testing and metamorphic testing). The key idea is to generate many simple variants of a program that initially passes verification. All variants are, by construction, equivalent to the original program; thus, any variant that fails verification indicates lack of robustness in the verifier. We implemented our technique in a tool called "mugie", which operates on programs written in the popular Boogie language for verification-used as intermediate representation in numerous program verifiers. Experiments targeting 135 Boogie programs indicate that brittle behavior occurs fairly frequently (16 programs) and is not hard to trigger. Based on these results, the paper discusses the main sources of brittle behavior and suggests means of improving robustness.

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arXiv2016

Bayesian Statistics in Software Engineering: Practical Guide and Case Studies

Statistics comes in two main flavors: frequentist and Bayesian. For historical and technical reasons, frequentist statistics has dominated data analysis in the past; but Bayesian statistics is making a comeback at the forefront of science. In this paper, we give a practical overview of Bayesian statistics and illustrate its main advantages over frequentist statistics for the kinds of analyses that are common in empirical software engineering, where frequentist statistics still is standard. We also apply Bayesian statistics to empirical data from previous research investigating agile vs. structured development processes, the performance of programming languages, and random testing of object-oriented programs. In addition to being case studies demonstrating how Bayesian analysis can be applied in practice, they provide insights beyond the results in the original publications (which used frequentist statistics), thus showing the practical value brought by Bayesian statistics.

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arXiv2016

Why Just Boogie? Translating Between Intermediate Verification Languages

The verification systems Boogie and Why3 use their respective intermediate languages to generate verification conditions from high-level programs. Since the two systems support different back-end provers (such as Z3 and Alt-Ergo) and are used to encode different high-level languages (such as C# and Java), being able to translate between their intermediate languages would provide a way to reuse one system's features to verify programs meant for the other. This paper describes a translation of Boogie into WhyML (Why3's intermediate language) that preserves semantics, verifiability, and program structure to a large degree. We implemented the translation as a tool and applied it to 194 Boogie-verified programs of various sources and sizes; Why3 verified 83% of the translated programs with the same outcome as Boogie. These results indicate that the translation is often effective and practically applicable.

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arXiv2016

Inferring Loop Invariants by Mutation, Dynamic Analysis, and Static Checking

Verifiers that can prove programs correct against their full functional specification require, for programs with loops, additional annotations in the form of loop invariants---propeties that hold for every iteration of a loop. We show that significant loop invariant candidates can be generated by systematically mutating postconditions; then, dynamic checking (based on automatically generated tests) weeds out invalid candidates, and static checking selects provably valid ones. We present a framework that automatically applies these techniques to support a program prover, paving the way for fully automatic verification without manually written loop invariants: Applied to 28 methods (including 39 different loops) from various java.util classes (occasionally modified to avoid using Java features not fully supported by the static checker), our DYNAMATE prototype automatically discharged 97% of all proof obligations, resulting in automatic complete correctness proofs of 25 out of the 28 methods---outperforming several state-of-the-art tools for fully automatic verification.

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arXiv2015

The AutoProof Verifier: Usability by Non-Experts and on Standard Code

Formal verification tools are often developed by experts for experts; as a result, their usability by programmers with little formal methods experience may be severely limited. In this paper, we discuss this general phenomenon with reference to AutoProof: a tool that can verify the full functional correctness of object-oriented software. In particular, we present our experiences of using AutoProof in two contrasting contexts representative of non-expert usage. First, we discuss its usability by students in a graduate course on software verification, who were tasked with verifying implementations of various sorting algorithms. Second, we evaluate its usability in verifying code developed for programming assignments of an undergraduate course. The first scenario represents usability by serious non-experts; the second represents usability on "standard code", developed without full functional verification in mind. We report our experiences and lessons learnt, from which we derive some general suggestions for furthering the development of verification tools with respect to improving their usability.

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arXiv2015

Rotation of Sequences: Algorithms and Proofs

Sequence rotation consists of a circular shift of the sequence's elements by a given number of positions. We present the four classic algorithms to rotate a sequence; the loop invariants underlying their correctness; detailed correctness proofs; and fully annotated versions for the verifiers Boogie, Dafny, and ESC/Java2. The presentation illustrates in detail both how the algorithms work and what it takes to carry out mechanized proofs of their correctness.

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arXiv2015

A Comparative Study of Programming Languages in Rosetta Code

Sometimes debates on programming languages are more religious than scientific. Questions about which language is more succinct or efficient, or makes developers more productive are discussed with fervor, and their answers are too often based on anecdotes and unsubstantiated beliefs. In this study, we use the largely untapped research potential of Rosetta Code, a code repository of solutions to common programming tasks in various languages, to draw a fair and well-founded comparison. Rosetta Code offers a large data set for analysis. Our study is based on 7087 solution programs corresponding to 745 tasks in 8 widely used languages representing the major programming paradigms (procedural: C and Go; object-oriented: C# and Java; functional: F# and Haskell; scripting: Python and Ruby). Our statistical analysis reveals, most notably, that: functional and scripting languages are more concise than procedural and object-oriented languages; C is hard to beat when it comes to raw speed on large inputs, but performance differences over inputs of moderate size are less pronounced and allow even interpreted languages to be competitive; compiled strongly-typed languages, where more defects can be caught at compile time, are less prone to runtime failures than interpreted or weakly-typed languages. We discuss implications of these results for developers, language designers, and educators.

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arXiv2015

AutoProof: Auto-active Functional Verification of Object-oriented Programs

Auto-active verifiers provide a level of automation intermediate between fully automatic and interactive: users supply code with annotations as input while benefiting from a high level of automation in the back-end. This paper presents AutoProof, a state-of-the-art auto-active verifier for object-oriented sequential programs with complex functional specifications. AutoProof fully supports advanced object-oriented features and a powerful methodology for framing and class invariants, which make it applicable in practice to idiomatic object-oriented patterns. The paper focuses on describing AutoProof's interface, design, and implementation features, and demonstrates AutoProof's performance on a rich collection of benchmark problems. The results attest AutoProof's competitiveness among tools in its league on cutting-edge functional verification of object-oriented programs.

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arXiv2014

Bounded Variability of Metric Temporal Logic

Previous work has shown that reasoning with real-time temporal logics is often simpler when restricted to models with bounded variability---where no more than v events may occur every V time units, for given v, V. When reasoning about formulas with intrinsic bounded variability, one can employ the simpler techniques that rely on bounded variability, without any loss of generality. What is then the complexity of algorithmically deciding which formulas have intrinsic bounded variability? In this paper, we study the problem with reference to Metric Temporal Logic (MTL). We prove that deciding bounded variability of MTL formulas is undecidable over dense-time models, but with a undecidability degree lower than generic dense-time MTL satisfiability. Over discrete-time models, instead, deciding MTL bounded variability has the same exponential-space complexity as satisfiability. To complement these negative results, we also briefly discuss small fragments of MTL that are more amenable to reasoning about bounded variability.

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arXiv2014

Automated Fixing of Programs with Contracts

This paper describes AutoFix, an automatic debugging technique that can fix faults in general-purpose software. To provide high-quality fix suggestions and to enable automation of the whole debugging process, AutoFix relies on the presence of simple specification elements in the form of contracts (such as pre- and postconditions). Using contracts enhances the precision of dynamic analysis techniques for fault detection and localization, and for validating fixes. The only required user input to the AutoFix supporting tool is then a faulty program annotated with contracts; the tool produces a collection of validated fixes for the fault ranked according to an estimate of their suitability. In an extensive experimental evaluation, we applied AutoFix to over 200 faults in four code bases of different maturity and quality (of implementation and of contracts). AutoFix successfully fixed 42% of the faults, producing, in the majority of cases, corrections of quality comparable to those competent programmers would write; the used computational resources were modest, with an average time per fix below 20 minutes on commodity hardware. These figures compare favorably to the state of the art in automated program fixing, and demonstrate that the AutoFix approach is successfully applicable to reduce the debugging burden in real-world scenarios.

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arXiv2014

Contracts in Practice

Contracts are a form of lightweight formal specification embedded in the program text. Being executable parts of the code, they encourage programmers to devote proper attention to specifications, and help maintain consistency between specification and implementation as the program evolves. The present study investigates how contracts are used in the practice of software development. Based on an extensive empirical analysis of 21 contract-equipped Eiffel, C#, and Java projects totaling more than 260 million lines of code over 7700 revisions, it explores, among other questions: 1) which kinds of contract elements (preconditions, postconditions, class invariants) are used more often; 2) how contracts evolve over time; 3) the relationship between implementation changes and contract changes; and 4) the role of inheritance in the process. It has found, among other results, that: the percentage of program elements that include contracts is above 33% for most projects and tends to be stable over time; there is no strong preference for a certain type of contract element; contracts are quite stable compared to implementations; and inheritance does not significantly affect qualitative trends of contract usage.

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arXiv2014

Flexible Invariants Through Semantic Collaboration

Modular reasoning about class invariants is challenging in the presence of dependencies among collaborating objects that need to maintain global consistency. This paper presents semantic collaboration: a novel methodology to specify and reason about class invariants of sequential object-oriented programs, which models dependencies between collaborating objects by semantic means. Combined with a simple ownership mechanism and useful default schemes, semantic collaboration achieves the flexibility necessary to reason about complicated inter-object dependencies but requires limited annotation burden when applied to standard specification patterns. The methodology is implemented in AutoProof, our program verifier for the Eiffel programming language (but it is applicable to any language supporting some form of representation invariants). An evaluation on several challenge problems proposed in the literature demonstrates that it can handle a variety of idiomatic collaboration patterns, and is more widely applicable than the existing invariant methodologies.

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