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Martin Monperrus

Publications and source records attributed to Martin Monperrus.

At least 163 records · Page 9Linked to original sources

A Generative Model of Software Dependency Graphs to Better Understand Software Evolution

Software systems are composed of many interacting elements. A natural way to abstract over software systems is to model them as graphs. In this paper we consider software dependency graphs of object-oriented software and we study one topological property: the degree distribution. Based on the analysis of ten software systems written in Java, we show that there exists completely different systems that have the same degree distribution. Then, we propose a generative model of software dependency graphs which synthesizes graphs whose degree distribution is close to the empirical ones observed in real software systems. This model gives us novel insights on the potential fundamental rules of software evolution.

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Test Case Generation for Program Repair: A Study of Feasibility and Effectiveness

Among the many different kinds of program repair techniques, one widely studied family of techniques is called test suite based repair. Test-suites are in essence input-output specifications and are therefore typically inadequate for completely specifying the expected behavior of the program under repair. Consequently, the patches generated by test suite based program repair techniques pass the test suite, yet may be incorrect. Patches that are overly specific to the used test suite and fail to generalize to other test cases are called overfitting patches. In this paper, we investigate the feasibility and effectiveness of test case generation in alleviating the overfitting issue. We propose two approaches for using test case generation to improve test suite based repair, and perform an extensive evaluation of the effectiveness of the proposed approaches in enabling better test suite based repair on 224 bugs of the Defects4J repository. The results indicate that test case generation can change the resulting patch, but is not effective at turning incorrect patches into correct ones. We identify the problems related with the ineffectiveness, and anticipate that our results and findings will lead to future research to build test-case generation techniques that are tailored to automatic repair systems.

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BanditRepair: Speculative Exploration of Runtime Patches

We propose, BanditRepair, a system that systematically explores and assesses a set of possible runtime patches. The system is grounded on so-called bandit algorithms, that are online machine learning algorithms, designed for constantly balancing exploitation and exploration. BanditRepair's runtime patches are based on modifying the execution state for repairing null dereferences. BanditRepair constantly trades the ratio of automatically handled failures for searching for new runtime patches and vice versa. We evaluate the system with 16 null dereference field bugs, where BanditRepair identifies a total of 8460 different runtime patches, which are composed of 1 up to 8 decisions (execution modifications) taken in a row. We are the first to finely characterize the search space and the outcomes of runtime repair based on execution modification.

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Automatic Repair of Real Bugs: An Experience Report on the Defects4J Dataset

Defects4J is a large, peer-reviewed, structured dataset of real-world Java bugs. Each bug in Defects4J is provided with a test suite and at least one failing test case that triggers the bug. In this paper, we report on an experiment to explore the effectiveness of automatic repair on Defects4J. The result of our experiment shows that 47 bugs of the Defects4J dataset can be automatically repaired by state-of- the-art repair. This sets a baseline for future research on automatic repair for Java. We have manually analyzed 84 different patches to assess their real correctness. In total, 9 real Java bugs can be correctly fixed with test-suite based repair. This analysis shows that test-suite based repair suffers from under-specified bugs, for which trivial and incorrect patches still pass the test suite. With respect to practical applicability, it takes in average 14.8 minutes to find a patch. The experiment was done on a scientific grid, totaling 17.6 days of computation time. All their systems and experimental results are publicly available on Github in order to facilitate future research on automatic repair.

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NPEFix: Automatic Runtime Repair of Null Pointer Exceptions in Java

Null pointer exceptions, also known as null dereferences are the number one exceptions in the field. In this paper, we propose 9 alternative execution semantics when a null pointer exception is about to happen. We implement those alternative execution strategies using code transformation in a tool called NPEfix. We evaluate our prototype implementation on 11 field null dereference bugs and 519 seeded failures and show that NPEfix is able to repair at runtime 10/11 and 318/519 failures.

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Casper: Debugging Null Dereferences with Dynamic Causality Traces

Fixing a software error requires understanding its root cause. In this paper, we introduce ''causality traces'', crafted execution traces augmented with the information needed to reconstruct the causal chain from the root cause of a bug to an execution error. We propose an approach and a tool, called Casper, for dynamically constructing causality traces for null dereference errors. The core idea of Casper is to inject special values, called ''ghosts'', into the execution stream to construct the causality trace at runtime. We evaluate our contribution by providing and assessing the causality traces of 14 real null dereference bugs collected over six large, popular open-source projects. Over this data set, Casper builds a causality trace in less than 5 seconds.

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DSpot: Test Amplification for Automatic Assessment of Computational Diversity

Context: Computational diversity, i.e., the presence of a set of programs that all perform compatible services but that exhibit behavioral differences under certain conditions, is essential for fault tolerance and security. Objective: We aim at proposing an approach for automatically assessing the presence of computational diversity. In this work, computationally diverse variants are defined as (i) sharing the same API, (ii) behaving the same according to an input-output based specification (a test-suite) and (iii) exhibiting observable differences when they run outside the specified input space. Method: Our technique relies on test amplification. We propose source code transformations on test cases to explore the input domain and systematically sense the observation domain. We quantify computational diversity as the dissimilarity between observations on inputs that are outside the specified domain. Results: We run our experiments on 472 variants of 7 classes from open-source, large and thoroughly tested Java classes. Our test amplification multiplies by ten the number of input points in the test suite and is effective at detecting software diversity. Conclusion: The key insights of this study are: the systematic exploration of the observable output space of a class provides new insights about its degree of encapsulation; the behavioral diversity that we observe originates from areas of the code that are characterized by their flexibility (caching, checking, formatting, etc.).

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Dynamic Analysis can be Improved with Automatic Test Suite Refactoring

Context: Developers design test suites to automatically verify that software meets its expected behaviors. Many dynamic analysis techniques are performed on the exploitation of execution traces from test cases. However, in practice, there is only one trace that results from the execution of one manually-written test case. Objective: In this paper, we propose a new technique of test suite refactoring, called B-Refactoring. The idea behind B-Refactoring is to split a test case into small test fragments, which cover a simpler part of the control flow to provide better support for dynamic analysis. Method: For a given dynamic analysis technique, our test suite refactoring approach monitors the execution of test cases and identifies small test cases without loss of the test ability. We apply B-Refactoring to assist two existing analysis tasks: automatic repair of if-statements bugs and automatic analysis of exception contracts. Results: Experimental results show that test suite refactoring can effectively simplify the execution traces of the test suite. Three real-world bugs that could previously not be fixed with the original test suite are fixed after applying B-Refactoring; meanwhile, exception contracts are better verified via applying B-Refactoring to original test suites. Conclusions: We conclude that applying B-Refactoring can effectively improve the purity of test cases. Existing dynamic analysis tasks can be enhanced by test suite refactoring.

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Automatic Repair of Infinite Loops

Research on automatic software repair is concerned with the development of systems that automatically detect and repair bugs. One well-known class of bugs is the infinite loop. Every computer programmer or user has, at least once, experienced this type of bug. We state the problem of repairing infinite loops in the context of test-suite based software repair: given a test suite with at least one failing test, generate a patch that makes all test cases pass. Consequently, repairing infinites loop means having at least one test case that hangs by triggering the infinite loop. Our system to automatically repair infinite loops is called $Infinitel$. We develop a technique to manipulate loops so that one can dynamically analyze the number of iterations of loops; decide to interrupt the loop execution; and dynamically examine the state of the loop on a per-iteration basis. Then, in order to synthesize a new loop condition, we encode this set of program states as a code synthesis problem using a technique based on Satisfiability Modulo Theory (SMT). We evaluate our technique on seven seeded-bugs and on seven real-bugs. $Infinitel$ is able to repair all of them, within seconds up to one hour on a standard laptop configuration.

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Software that Learns from its Own Failures

All non-trivial software systems suffer from unanticipated production failures. However, those systems are passive with respect to failures and do not take advantage of them in order to improve their future behavior: they simply wait for them to happen and trigger hard-coded failure recovery strategies. Instead, I propose a new paradigm in which software systems learn from their own failures. By using an advanced monitoring system they have a constant awareness of their own state and health. They are designed in order to automatically explore alternative recovery strategies inferred from past successful and failed executions. Their recovery capabilities are assessed by self-injection of controlled failures; this process produces knowledge in prevision of future unanticipated failures.

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ASTOR: Evolutionary Automatic Software Repair for Java

Context: During last years, many automatic software repair approaches have been presented by the software engineering research community. According to the corresponding papers, these approaches are able to repair real defects from open source projects. Problematic: Some previous publications in the automatic repair field do not provide the implementation of theirs approaches. Consequently, it is not possible for the research community to re-execute the original evaluation, to set up new evaluations (for example, to evaluate the performance against new defects) or to compare approaches against each others. Solution: We propose a publicly available automatic software repair tool called Astor. It implements three state-of-the-art automatic software repair approaches in the context of Java programs (including GenProg and a subset of PAR's templates). The source code of Astor is licensed under the GNU General Public Licence (GPL v2).

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The Multiple Facets of Software Diversity: Recent Developments in Year 2000 and Beyond

Early experiments with software diversity in the mid 1970's investigated N-version programming and recovery blocks to increase the reliability of embedded systems. Four decades later, the literature about software diversity has expanded in multiple directions: goals (fault-tolerance, security, software engineering); means (managed or automated diversity) and analytical studies (quantification of diversity and its impact). Our paper contributes to the field of software diversity as the first paper that adopts an inclusive vision of the area, with an emphasis on the most recent advances in the field. This survey includes classical work about design and data diversity for fault tolerance, as well as the cybersecurity literature that investigates randomization at different system levels. It broadens this standard scope of diversity, to include the study and exploitation of natural diversity and the management of diverse software products. Our survey includes the most recent works, with an emphasis from 2000 to present. The targeted audience is researchers and practitioners in one of the surveyed fields, who miss the big picture of software diversity. Assembling the multiple facets of this fascinating topic sheds a new light on the field.

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Test Case Purification for Improving Fault Localization

Finding and fixing bugs are time-consuming activities in software development. Spectrum-based fault localization aims to identify the faulty position in source code based on the execution trace of test cases. Failing test cases and their assertions form test oracles for the failing behavior of the system under analysis. In this paper, we propose a novel concept of spectrum driven test case purification for improving fault localization. The goal of test case purification is to separate existing test cases into small fractions (called purified test cases) and to enhance the test oracles to further localize faults. Combining with an original fault localization technique (e.g., Tarantula), test case purification results in better ranking the program statements. Our experiments on 1800 faults in six open-source Java programs show that test case purification can effectively improve existing fault localization techniques.

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Static Analysis for Extracting Permission Checks of a Large Scale Framework: The Challenges And Solutions for Analyzing Android

A common security architecture is based on the protection of certain resources by permission checks (used e.g., in Android and Blackberry). It has some limitations, for instance, when applications are granted more permissions than they actually need, which facilitates all kinds of malicious usage (e.g., through code injection). The analysis of permission-based framework requires a precise mapping between API methods of the framework and the permissions they require. In this paper, we show that naive static analysis fails miserably when applied with off-the-shelf components on the Android framework. We then present an advanced class-hierarchy and field-sensitive set of analyses to extract this mapping. Those static analyses are capable of analyzing the Android framework. They use novel domain specific optimizations dedicated to Android.

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A Critical Review of "Automatic Patch Generation Learned from Human-Written Patches": Essay on the Problem Statement and the Evaluation of Automatic Software Repair

At ICSE'2013, there was the first session ever dedicated to automatic program repair. In this session, Kim et al. presented PAR, a novel template-based approach for fixing Java bugs. We strongly disagree with key points of this paper. Our critical review has two goals. First, we aim at explaining why we disagree with Kim and colleagues and why the reasons behind this disagreement are important for research on automatic software repair in general. Second, we aim at contributing to the field with a clarification of the essential ideas behind automatic software repair. In particular we discuss the main evaluation criteria of automatic software repair: understandability, correctness and completeness. We show that depending on how one sets up the repair scenario, the evaluation goals may be contradictory. Eventually, we discuss the nature of fix acceptability and its relation to the notion of software correctness.

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Automatic Repair of Buggy If Conditions and Missing Preconditions with SMT

We present Nopol, an approach for automatically repairing buggy if conditions and missing preconditions. As input, it takes a program and a test suite which contains passing test cases modeling the expected behavior of the program and at least one failing test case embodying the bug to be repaired. It consists of collecting data from multiple instrumented test suite executions, transforming this data into a Satisfiability Modulo Theory (SMT) problem, and translating the SMT result -- if there exists one -- into a source code patch. Nopol repairs object oriented code and allows the patches to contain nullness checks as well as specific method calls.

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Do the Fix Ingredients Already Exist? An Empirical Inquiry into the Redundancy Assumptions of Program Repair Approaches

Much initial research on automatic program repair has focused on experimental results to probe their potential to find patches and reduce development effort. Relatively less effort has been put into understanding the hows and whys of such approaches. For example, a critical assumption of the GenProg technique is that certain bugs can be fixed by copying and re-arranging existing code. In other words, GenProg assumes that the fix ingredients already exist elsewhere in the code. In this paper, we formalize these assumptions around the concept of ''temporal redundancy''. A temporally redundant commit is only composed of what has already existed in previous commits. Our experiments show that a large proportion of commits that add existing code are temporally redundant. This validates the fundamental redundancy assumption of GenProg.

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An Approach for Discovering Traceability Links between Regulatory Documents and Source Code Through User-Interface Labels

In application domains that are regulated, software vendors must maintain traceability links between the regulatory items and the code base implementing them. In this paper, we present a traceability approach based on the intuition that the regulatory documents and the user-interface of the corresponding software applications are very close. First, they use the same terminology. Second, most important regulatory pieces of information appear in the graphical user-interface because the end-users in those application domains care about the regulation (by construction). We evaluate our approach in the domain of green building. The evaluation involves a domain expert, lead architect of a commercial product within this area. The evaluation shows that the recovered traceability links are accurate.

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