Systematic techniques for more effective fault localization and program repair

Debugging faulty code is a tedious process that is often quite expensive and can require much manual effort. Developers typically perform debugging in two key steps: (1) fault localization, i.e., identifying the location of faulty line(s) of code; and (2) program repair, i.e., modifying the code to remove the fault(s). Automating debugging to reduce its cost has been the focus of a number of research projects during the last decade, which have introduced a variety of techniques. However, existing techniques suffer from two basic limitations. One, they lack accuracy to handle real programs. Two, they focus on automating only one of the two key steps, thereby leaving the other key step to the developer. Our thesis is that an approach that integrates systematic search based on state-of-the-art constraint solvers with techniques to analyze artifacts that describe application specific properties and behaviors, provides the basis for developing more effective debugging techniques. We focus on faults in programs that operate on structurally complex inputs, such as heap-allocated data or relational databases. Our approach lays the foundation for a unified framework for localization and repair of faults in programs. We embody our thesis in a suite of integrated techniques based on propositional satisfiability solving, correctness specifications analysis, test-spectra analysis, and rule-learning algorithms from machine learning, implement them as a prototype tool-set, and evaluate them using several subject programs.Electrical and Computer Engineerin

Learning to Combine Multiple Ranking Metrics for Fault Localization

International audienceFault localization is an inevitable step in software debugging. Spectrum-based fault localization consists in computing a ranking metric on execution traces to identify faulty source code. Existing empirical studies on fault localization show that there is no optimal ranking metric for all faults in practice. In this paper, we propose Multric, a learning-based approach to combining multiple ranking metrics for effective fault localization. In Multric, a suspiciousness score of a program entity is a combination of existing ranking metrics. Multric consists two major phases: learning and ranking. Based on training faults, Multric builds a ranking model by learning from pairs of faulty and non-faulty source code elements. When a new fault appears, Multric computes the final ranking with the learned model. Experiments are conducted on 5386 seeded faults in ten open-source Java programs. We empirically compare Multric against four widely-studied metrics and three recently-proposed one. Our experimental results show that Multric localizes faults more effectively than state-of-art metrics, such as Tarantula, Ochiai, and Ample

Computer Aided Verification

The open access two-volume set LNCS 12224 and 12225 constitutes the refereed proceedings of the 32st International Conference on Computer Aided Verification, CAV 2020, held in Los Angeles, CA, USA, in July 2020.* The 43 full papers presented together with 18 tool papers and 4 case studies, were carefully reviewed and selected from 240 submissions. The papers were organized in the following topical sections: Part I: AI verification; blockchain and Security; Concurrency; hardware verification and decision procedures; and hybrid and dynamic systems. Part II: model checking; software verification; stochastic systems; and synthesis. *The conference was held virtually due to the COVID-19 pandemic

Practical Network Programming Automation

Network configurations are notoriously hard to write and maintain correctly. It requiresexpertise about the domain to write, frequent and laborious updates, and sometimes formal proof to ensure the absence of certain mistakes. The problem becomes more challenging with the popularity of software-defined network(SDN) in recent years, which aims to give users more flexible control over the network’s dynamic behaviors. There has been research on automating the process of configuring the network. However, much of it requires users to learn a specific programming abstraction or interface. Since network operators are a group generally unfamiliar with programming, using these systems may go beyond their abilities. It is also hard to ensure these systems are scalable and accurate enough for real-world usecases. They mostly lack both design considerations to address scalability and accuracy, and also a systematic evaluation of the two metrics in practical scenarios. In this work, we propose a series of approaches to automate network programming. They are based on specifications that are easy and natural to obtain by network operators. We also apply novel program analysis techniques to speed up the process of finding a program that can accurately capture the intention of the specification. We have evaluated our systems on a broad range of benchmarks obtained from real-world data. They have shown ability to finish complex programming tasks within minutes and achieved very high accuracy

SIZE-SENSITIVE CRYSTAL PLASTICITY FINITE ELEMENT FRAMEWORK FOR SIMULATING BEHAVIOR OF LAMELLAR METAL-METAL COMPOSITES

Growing demands for materials with enhanced and superb characteristics increase the difficulty and the amount of research necessary to be conducted in many different areas of expertise. The vast field of computational mechanics represents a significant source of valuable solutions to many of these challenges and can provide a smoother transition in the process when a new material is introduced. Experimental techniques are not always able to measure the localized material features due to the very complicated deformation conditions. As an alternative approach, full-field models are developed, such the ones contained in this dissertation that can bridge this gap and provide source of significant insights. The crystal plasticity finite element models (CPFEM) developed under this dissertation are presented and discussed through several specific case studies, which establish the fundamental microstructure-property relationships that describe in particular the deformation behavior of novel multilayer metallic lamellar microstructures composed of Zirconium-Niobium and Magnesium-Niobium layers. These lamellar material systems exhibit extraordinary strength while preserving ductility and they are promising candidates for application in many industries, such as nuclear and automotive. Different formulations of the 3D multiscale models were numerically implemented to investigate the origin and the development of the microstructural features that occured during the fabrication process of these lamellar composites. In particular, the orientation stability of nanocrystalline Zirconium and the formation of strain localizations were investigated during accumulative roll bonding process. Furthermore, the work contained in this dissertation describes the first attempt to incorporate the confined layer slip (CLS) model into CPFE, which greatly contributes to fundamental understanding of how Magnesium-Niobium nano-layered composites deform elastically and plastically at nanometer length scales. Next, significant efforts were put into investigating a mechanism of deformation twinning. This deformation mechanism governs the mechanical behavior of many polycrystalline metals, particularly those with low symmetry crystal structures. Deformation twins are represented as lamellar inclusions in the granular microstructures, and overall the material behaves as a composite. Hence, a novel modeling approach, which explicitly models the formation and thickening of a twin lamella within a crystal plasticity finite element framework was developed. The model represents a unique numerical procedure which is able to relate spatially resolved fields of stress and strains with microstructural changes during a twin formation and thickening. This approach was applied to study the twin formation and thickening in cast Uranium and Magnesium alloy AZ31. In AZ31 the effects of dislocation density on a twin propagation were investigated, as well as the influence of the double twin formation on the material’s fracture behavior. Overall, the presented work in my dissertation provides a powerful predictive simulation tool that could be used in many subsequent studies contributing to the further advancements in the field of computational material science