3,088 research outputs found
Automating property-based testing of evolving web services
Web services are the most widely used service technology that drives the Service-Oriented Computing~(SOC) paradigm. As a result, effective testing of web services is getting increasingly important. In this paper, we present a framework and toolset for testing web services and for evolving test code in sync with the evolution of web services. Our approach to testing web services is based on the Erlang programming language and QuviQ QuickCheck, a property-based testing tool written in Erlang, and our support for test code evolution is added to Wrangler, the Erlang refactoring tool.
The key components of our system include the automatic generation of initial test code, the inference of web service interface changes between versions, the provision of a number of domain specific refactorings and the automatic generation of refactoring scripts for evolving the test code. Our framework provides users with a powerful and expressive web service testing framework, while minimising users' effort in creating, maintaining and evolving the test model. The framework presented in this paper can be used by both web service providers and consumers, and can be used to test web services written in whatever language; the approach advocated here could also be adopted in other property-based testing frameworks and refactoring tools
Functional Testing Approaches for "BIFST-able" tlm_fifo
Evolution of Electronic System Level design methodologies, allows a wider use of Transaction-Level Modeling (TLM). TLM is a high-level approach to modeling digital systems that emphasizes on separating communications among modules from the details of functional units. This paper explores different functional testing approaches for the implementation of Built-in Functional Self Test facilities in the TLM primitive channel tlm_fifo. In particular, it focuses on three different test approaches based on a finite state machine model of tlm_fifo, functional fault models, and march tests respectivel
Robust Computer Algebra, Theorem Proving, and Oracle AI
In the context of superintelligent AI systems, the term "oracle" has two
meanings. One refers to modular systems queried for domain-specific tasks.
Another usage, referring to a class of systems which may be useful for
addressing the value alignment and AI control problems, is a superintelligent
AI system that only answers questions. The aim of this manuscript is to survey
contemporary research problems related to oracles which align with long-term
research goals of AI safety. We examine existing question answering systems and
argue that their high degree of architectural heterogeneity makes them poor
candidates for rigorous analysis as oracles. On the other hand, we identify
computer algebra systems (CASs) as being primitive examples of domain-specific
oracles for mathematics and argue that efforts to integrate computer algebra
systems with theorem provers, systems which have largely been developed
independent of one another, provide a concrete set of problems related to the
notion of provable safety that has emerged in the AI safety community. We
review approaches to interfacing CASs with theorem provers, describe
well-defined architectural deficiencies that have been identified with CASs,
and suggest possible lines of research and practical software projects for
scientists interested in AI safety.Comment: 15 pages, 3 figure
Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis
Even with impressive advances in automated formal methods, certain problems
in system verification and synthesis remain challenging. Examples include the
verification of quantitative properties of software involving constraints on
timing and energy consumption, and the automatic synthesis of systems from
specifications. The major challenges include environment modeling,
incompleteness in specifications, and the complexity of underlying decision
problems.
This position paper proposes sciduction, an approach to tackle these
challenges by integrating inductive inference, deductive reasoning, and
structure hypotheses. Deductive reasoning, which leads from general rules or
concepts to conclusions about specific problem instances, includes techniques
such as logical inference and constraint solving. Inductive inference, which
generalizes from specific instances to yield a concept, includes algorithmic
learning from examples. Structure hypotheses are used to define the class of
artifacts, such as invariants or program fragments, generated during
verification or synthesis. Sciduction constrains inductive and deductive
reasoning using structure hypotheses, and actively combines inductive and
deductive reasoning: for instance, deductive techniques generate examples for
learning, and inductive reasoning is used to guide the deductive engines.
We illustrate this approach with three applications: (i) timing analysis of
software; (ii) synthesis of loop-free programs, and (iii) controller synthesis
for hybrid systems. Some future applications are also discussed
Automatic Software Repair: a Bibliography
This article presents a survey on automatic software repair. Automatic
software repair consists of automatically finding a solution to software bugs
without human intervention. This article considers all kinds of repairs. First,
it discusses behavioral repair where test suites, contracts, models, and
crashing inputs are taken as oracle. Second, it discusses state repair, also
known as runtime repair or runtime recovery, with techniques such as checkpoint
and restart, reconfiguration, and invariant restoration. The uniqueness of this
article is that it spans the research communities that contribute to this body
of knowledge: software engineering, dependability, operating systems,
programming languages, and security. It provides a novel and structured
overview of the diversity of bug oracles and repair operators used in the
literature
Automating test oracles generation
Software systems play a more and more important role in our everyday life. Many relevant human activities nowadays involve the execution of a piece of software. Software has to be reliable to deliver the expected behavior, and assessing the quality of software is of primary importance to reduce the risk of runtime errors. Software testing is the most common quality assessing technique for software. Testing consists in running the system under test on a finite set of inputs, and checking the correctness of the results. Thoroughly testing a software system is expensive and requires a lot of manual work to define test inputs (stimuli used to trigger different software behaviors) and test oracles (the decision procedures checking the correctness of the results). Researchers have addressed the cost of testing by proposing techniques to automatically generate test inputs. While the generation of test inputs is well supported, there is no way to generate cost-effective test oracles: Existing techniques to produce test oracles are either too expensive to be applied in practice, or produce oracles with limited effectiveness that can only identify blatant failures like system crashes. Our intuition is that cost-effective test oracles can be generated using information produced as a byproduct of the normal development activities. The goal of this thesis is to create test oracles that can detect faults leading to semantic and non-trivial errors, and that are characterized by a reasonable generation cost. We propose two ways to generate test oracles, one derives oracles from the software redundancy and the other from the natural language comments that document the source code of software systems. We present a technique that exploits redundant sequences of method calls encoding the software redundancy to automatically generate test oracles named CCOracles. We describe how CCOracles are automatically generated, deployed, and executed. We prove the effectiveness of CCOracles by measuring their fault-finding effectiveness when combined with both automatically generated and hand-written test inputs. We also present Toradocu, a technique that derives executable specifications from Javadoc comments of Java constructors and methods. From such specifications, Toradocu generates test oracles that are then deployed into existing test suites to assess the outputs of given test inputs. We empirically evaluate Toradocu, showing that Toradocu accurately translates Javadoc comments into procedure specifications. We also show that Toradocu oracles effectively identify semantic faults in the SUT. CCOracles and Toradocu oracles stem from independent information sources and are complementary in the sense that they check different aspects of the system undertest
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