Test-Equivalence Analysis for Automatic Patch Generation

Automated program repair is a problem of finding a transformation (called a patch) of a given incorrect program that eliminates the observable failures. It has important applications such as providing debugging aids, automatically grading student assignments, and patching security vulnerabilities. A common challenge faced by existing repair techniques is scalability to large patch spaces, since there are many candidate patches that these techniques explicitly or implicitly consider. The correctness criteria for program repair is often given as a suite of tests. Current repair techniques do not scale due to the large number of test executions performed by the underlying search algorithms. In this work, we address this problem by introducing a methodology of patch generation based on a test-equivalence relation (if two programs are “test-equivalent” for a given test, they produce indistinguishable results on this test). We propose two test-equivalence relations based on runtime values and dependencies, respectively, and present an algorithm that performs on-the-fly partitioning of patches into test-equivalence classes. Our experiments on real-world programs reveal that the proposed methodology drastically reduces the number of test executions and therefore provides an order of magnitude efficiency improvement over existing repair techniques, without sacrificing patch quality

FixMiner: Mining Relevant Fix Patterns for Automated Program Repair

Patching is a common activity in software development. It is generally performed on a source code base to address bugs or add new functionalities. In this context, given the recurrence of bugs across projects, the associated similar patches can be leveraged to extract generic fix actions. While the literature includes various approaches leveraging similarity among patches to guide program repair, these approaches often do not yield fix patterns that are tractable and reusable as actionable input to APR systems. In this paper, we propose a systematic and automated approach to mining relevant and actionable fix patterns based on an iterative clustering strategy applied to atomic changes within patches. The goal of FixMiner is thus to infer separate and reusable fix patterns that can be leveraged in other patch generation systems. Our technique, FixMiner, leverages Rich Edit Script which is a specialized tree structure of the edit scripts that captures the AST-level context of the code changes. FixMiner uses different tree representations of Rich Edit Scripts for each round of clustering to identify similar changes. These are abstract syntax trees, edit actions trees, and code context trees. We have evaluated FixMiner on thousands of software patches collected from open source projects. Preliminary results show that we are able to mine accurate patterns, efficiently exploiting change information in Rich Edit Scripts. We further integrated the mined patterns to an automated program repair prototype, PARFixMiner, with which we are able to correctly fix 26 bugs of the Defects4J benchmark. Beyond this quantitative performance, we show that the mined fix patterns are sufficiently relevant to produce patches with a high probability of correctness: 81% of PARFixMiner's generated plausible patches are correct.Comment: 31 pages, 11 figure

Representational dissimilarity metric spaces for stochastic neural networks

Quantifying similarity between neural representations -- e.g. hidden layer activation vectors -- is a perennial problem in deep learning and neuroscience research. Existing methods compare deterministic responses (e.g. artificial networks that lack stochastic layers) or averaged responses (e.g., trial-averaged firing rates in biological data). However, these measures of deterministic representational similarity ignore the scale and geometric structure of noise, both of which play important roles in neural computation. To rectify this, we generalize previously proposed shape metrics (Williams et al. 2021) to quantify differences in stochastic representations. These new distances satisfy the triangle inequality, and thus can be used as a rigorous basis for many supervised and unsupervised analyses. Leveraging this novel framework, we find that the stochastic geometries of neurobiological representations of oriented visual gratings and naturalistic scenes respectively resemble untrained and trained deep network representations. Further, we are able to more accurately predict certain network attributes (e.g. training hyperparameters) from its position in stochastic (versus deterministic) shape space

OSS architecture for mixed-criticality systems – a dual view from a software and system engineering perspective

Computer-based automation in industrial appliances led to a growing number of logically dependent, but physically separated embedded control units per appliance. Many of those components are safety-critical systems, and require adherence to safety standards, which is inconsonant with the relentless demand for features in those appliances. Features lead to a growing amount of control units per appliance, and to a increasing complexity of the overall software stack, being unfavourable for safety certifications. Modern CPUs provide means to revise traditional separation of concerns design primitives: the consolidation of systems, which yields new engineering challenges that concern the entire software and system stack. Multi-core CPUs favour economic consolidation of formerly separated systems with one efficient single hardware unit. Nonetheless, the system architecture must provide means to guarantee the freedom from interference between domains of different criticality. System consolidation demands for architectural and engineering strategies to fulfil requirements (e.g., real-time or certifiability criteria) in safety-critical environments. In parallel, there is an ongoing trend to substitute ordinary proprietary base platform software components by mature OSS variants for economic and engineering reasons. There are fundamental differences of processual properties in development processes of OSS and proprietary software. OSS in safety-critical systems requires development process assessment techniques to build an evidence-based fundament for certification efforts that is based upon empirical software engineering methods. In this thesis, I will approach from both sides: the software and system engineering perspective. In the first part of this thesis, I focus on the assessment of OSS components: I develop software engineering techniques that allow to quantify characteristics of distributed OSS development processes. I show that ex-post analyses of software development processes can be used to serve as a foundation for certification efforts, as it is required for safety-critical systems. In the second part of this thesis, I present a system architecture based on OSS components that allows for consolidation of mixed-criticality systems on a single platform. Therefore, I exploit virtualisation extensions of modern CPUs to strictly isolate domains of different criticality. The proposed architecture shall eradicate any remaining hypervisor activity in order to preserve real-time capabilities of the hardware by design, while guaranteeing strict isolation across domains.Computergestützte Automatisierung industrieller Systeme führt zu einer wachsenden Anzahl an logisch abhängigen, aber physisch voneinander getrennten Steuergeräten pro System. Viele der Einzelgeräte sind sicherheitskritische Systeme, welche die Einhaltung von Sicherheitsstandards erfordern, was durch die unermüdliche Nachfrage an Funktionalitäten erschwert wird. Diese führt zu einer wachsenden Gesamtzahl an Steuergeräten, einhergehend mit wachsender Komplexität des gesamten Softwarekorpus, wodurch Zertifizierungsvorhaben erschwert werden. Moderne Prozessoren stellen Mittel zur Verfügung, welche es ermöglichen, das traditionelle >Trennung von Belangen< Designprinzip zu erneuern: die Systemkonsolidierung. Sie stellt neue ingenieurstechnische Herausforderungen, die den gesamten Software und Systemstapel betreffen. Mehrkernprozessoren begünstigen die ökonomische und effiziente Konsolidierung vormals getrennter Systemen zu einer effizienten Hardwareeinheit. Geeignete Systemarchitekturen müssen jedoch die Rückwirkungsfreiheit zwischen Domänen unterschiedlicher Kritikalität sicherstellen. Die Konsolidierung erfordert architektonische, als auch ingenieurstechnische Strategien um die Anforderungen (etwa Echtzeit- oder Zertifizierbarkeitskriterien) in sicherheitskritischen Umgebungen erfüllen zu können. Zunehmend werden herkömmliche proprietär entwickelte Basisplattformkomponenten aus ökonomischen und technischen Gründen vermehrt durch ausgereifte OSS Alternativen ersetzt. Jedoch hindern fundamentale Unterschiede bei prozessualen Eigenschaften des Entwicklungsprozesses bei OSS den Einsatz in sicherheitskritischen Systemen. Dieser erfordert Techniken, welche es erlauben die Entwicklungsprozesse zu bewerten um ein evidenzbasiertes Fundament für Zertifizierungsvorhaben basierend auf empirischen Methoden des Software Engineerings zur Verfügung zu stellen. In dieser Arbeit nähere ich mich von beiden Seiten: der Softwaretechnik, und der Systemarchitektur. Im ersten Teil befasse ich mich mit der Beurteilung von OSS Komponenten: Ich entwickle Softwareanalysetechniken, welche es ermöglichen, prozessuale Charakteristika von verteilten OSS Entwicklungsvorhaben zu quantifizieren. Ich zeige, dass rückschauende Analysen des Entwicklungsprozess als Grundlage für Softwarezertifizierungsvorhaben genutzt werden können. Im zweiten Teil dieser Arbeit widme ich mich der Systemarchitektur. Ich stelle eine OSS-basierte Systemarchitektur vor, welche die Konsolidierung von Systemen gemischter Kritikalität auf einer alleinstehenden Plattform ermöglicht. Dazu nutze ich Virtualisierungserweiterungen moderner Prozessoren aus, um die Hardware in strikt voneinander isolierten Rechendomänen unterschiedlicher Kritikalität unterteilen zu können. Die vorgeschlagene Architektur soll jegliche Betriebsstörungen des Hypervisors beseitigen, um die Echtzeitfähigkeiten der Hardware bauartbedingt aufrecht zu erhalten, während strikte Isolierung zwischen Domänen stets sicher gestellt ist

Automated Regression Testing and Verification of Complex Code Changes

Ph.DDOCTOR OF PHILOSOPH

A Neighborhood Analysis of Tree Growth and Survival in a Hurricane-Driven Tropical Forest

We present a likelihood-based regression method that was developed to analyze the effects of neighborhood competitive interactions and hurricane damage on tree growth and survival. The purpose of the method is to provide robust parameter estimates for a spatially explicit forest simulator and to gain insight into the processes that drive the patterns of species abundance in tropical forests. We test the method using census data from the 16-ha Luquillo Forest Dynamics Plot in Puerto Rico and describe effects of the spatial configuration, sizes, and species of neighboring trees on the growth and survival of 12 dominant tree species representing a variety of life history strategies. Variation in size-dependent growth and mortality suggests a complex relationship between size, growth, and survival under different regimes of light availability. Crowding effects on growth and survival appear to be idiosyncratic to each individual species, and with the exception of pioneers, there is little commonality among species that share similar life histories. We also explain the implications of differential susceptibility to hurricane damage on species' growth and survival and on their ability to respond to damage to neighboring trees. Tree species in the Luquillo Forest Dynamics Plot differ strikingly in both their susceptibility to hurricane disturbance and the nature of their recovery from wind disturbance, through response of both adult plants and juveniles to enhanced resource availability. At the stand level, intense competitive thinning of densely packed saplings that grew after hurricane damage accounted for the majority of post-hurricane mortality, particularly for shade-intolerant species. At the individual species level, effects of previous hurricane damage on growth and survival depended primarily on variation in the quantity and quality of hurricane damage sustained by target species and their interaction with life history characteristics of these individual species. Finally, we compare models that make different assumptions about the effects of competing species on tree growth and survival (e.g., equivalence of competitors vs. distinct species-specific effects). Size effects alone could not account for growth and survival for the majority of target species. Our results also demonstrate that competing species have distinct per capita effects on growth of dominant target species. In contrast, we found moderate support for a model that assumed functional equivalence of competitors on survival

Aeronautical Engineering. A continuing bibliography, supplement 115

This bibliography lists 273 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1979

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 23rd International Conference on Fundamental Approaches to Software Engineering, FASE 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 23 full papers, 1 tool paper and 6 testing competition papers presented in this volume were carefully reviewed and selected from 81 submissions. The papers cover topics such as requirements engineering, software architectures, specification, software quality, validation, verification of functional and non-functional properties, model-driven development and model transformation, software processes, security and software evolution