ترغب بنشر مسار تعليمي؟ اضغط هنا

Searching for Multi-Fault Programs in Defects4J

61   0   0.0 ( 0 )
 نشر من قبل Gabin An
 تاريخ النشر 2021
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

Defects4J has enabled numerous software testing and debugging research work since its introduction. A large part of its contribution, and the resulting popularity, lies in the clear separation and distillation of the root cause of each individual test failure based on careful manual analysis, which in turn allowed researchers to easily study individual faults in isolation. However, in a realistic debugging scenario, multiple faults can coexist and affect test results collectively. Study of automated debugging techniques for these situations, such as failure clustering or fault localisation for multiple faults, would significantly benefit from a reliable benchmark of multiple, coexisting faults. We search f

قيم البحث

اقرأ أيضاً

Computational models of human language often involve combinatorial problems. For instance, a probabilistic parser may marginalize over exponentially many trees to make predictions. Algorithms for such problems often employ dynamic programming and are not always unique. Finding one with optimal asymptotic runtime can be unintuitive, time-consuming, and error-prone. Our work aims to automate this laborious process. Given an initial correct declarative program, we search for a sequence of semantics-preserving transformations to improve its running time as much as possible. To this end, we describe a set of program transformations, a simple metric for assessing the efficiency of a transformed program, and a heuristic search procedure to improve this metric. We show that in practice, automated search -- like the mental search performed by human programmers -- can find substantial improvements to the initial program. Empirically, we show that many common speed-ups described in the NLP literature could have been discovered automatically by our system.
Bug patterns are erroneous code idioms or bad coding practices that have been proved to fail time and time again, which are usually caused by the misunderstanding of a programming languages features, the use of erroneous design patterns, or simple mi stakes sharing common behaviors. This paper identifies and categorizes some bug patterns in the quantum programming language Qiskit and briefly discusses how to eliminate or prevent those bug patterns. We take this research as the first step to provide an underlying basis for debugging and testing quantum programs.
The success of several constraint-based modeling languages such as OPL, ZINC, or COMET, appeals for better software engineering practices, particularly in the testing phase. This paper introduces a testing framework enabling automated test case gener ation for constraint programming. We propose a general framework of constraint program development which supposes that a first declarative and simple constraint model is available from the problem specifications analysis. Then, this model is refined using classical techniques such as constraint reformulation, surrogate and global constraint addition, or symmetry-breaking to form an improved constraint model that must be thoroughly tested before being used to address real-sized problems. We think that most of the faults are introduced in this refinement step and propose a process which takes the first declarative model as an oracle for detecting non-conformities. We derive practical test purposes from this process to generate automatically test data that exhibit non-conformities. We implemented this approach in a new tool called CPTEST that was used to automatically detect non-conformities on two classical benchmark programs, namely the Golomb rulers and the car-sequencing problem.
This paper presents an approach towards specifying and verifying adaptive distributed systems. We here take fault-handling as an example of adaptive behavior and propose a modeling language Sandal for describing fault-prone message-passing systems. O ne of the unique mechanisms of the language is a linguistic support for abstracting typical faults such as unexpected termination of processes and random loss of messages. The Sandal compiler translates a model into a set of NuSMV modules. During the compilation process, faults specified in the model will be woven into the output. One can thus enjoy full-automatic exhaustive fault-injection without writing faulty behaviors explicitly. We demonstrate the advantage of the language by verifying a model of the two-phase commit protocol under faulty environment.
We investigate the problem of classifying a line of program as containing a vulnerability or not using machine learning. Such a line-level classification task calls for a program representation which goes beyond reasoning from the tokens present in t he line. We seek a distributed representation in a latent feature space which can capture the control and data dependencies of tokens appearing on a line of program, while also ensuring lines of similar meaning have similar features. We present a neural architecture, Vulcan, that successfully demonstrates both these requirements. It extracts contextual information about tokens in a line and inputs them as Abstract Syntax Tree (AST) paths to a bi-directional LSTM with an attention mechanism. It concurrently represents the meanings of tokens in a line by recursively embedding the lines where they are most recently defined. In our experiments, Vulcan compares favorably with a state-of-the-art classifier, which requires significant preprocessing of programs, suggesting the utility of using deep learning to model program dependence information.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا