Model-Driven Engineering (MDE) is a broad discipline concerned with curating all aspects of system design using models. Model-Driven Architecture (MDA) is a highly publicised approach focusing on the generation of software systems from models. However, MDA consists of a large collection of complex, interlocking standards, which together are difficult to master and have only partial implementations. This motivated us to devise a much simpler language and toolset for MDE. The result is ReMoDeL (Reusable Model Design Language), a pure functional object-oriented language for describing concepts and relationships. ReMoDeL supports the creation of metamodels, models and model transformations. It leverages skills already known to programmers, such as inheritance and pure functional mapping. It integrates with any standard Java IDE and cross-compiles to Java, although ReMoDeL is more succinct (by 4x). ReMoDeL’s pure functional transformations are in principle amenable to formal proof by indu ction. Practically, it offers a convenient and fast way to prototype different metamodels and transformations. We are using ReMoDeL to develop alternatives to UML and MDA (with different models and abstraction levels), with promising results.

Cloud data centers seek to optimize their provision of pooled CPU, bandwidth and storage resources. While over-provision is wasteful, under-provision may lead to violating Service Level Agreements (SLAs) with their consumers; yet the relationship between low-level Key Performance Indicators (KPIs) and SLA violations is not well understood. State-of-the art monitoring systems typically react to service failures after the fact, partly due to unexpected nonlinearities in the aggregated performance data. We seek to provide better modelling of KPIs using predictive algorithms that could be used for the proactive monitoring and adaptation of cloud services. In this paper, we investigate the Boosted Decision Tree (BDT) regression algorithm. We tested the BDT algorithm in a real monitoring framework deployed on a novel Azure cloud test-bed distributed over multiple geolocations, using thousands of robot-user requests to produce huge volumes of KPI data. The BDT algorithm achieved an R-Squared score of 0.9991 at the 0.2 learning rate. This closely predicted the KPI data and outperformed other approaches, such as Ordinary Least Squares and Stochastic Gradient Descent; and is a promising candidate for making short- and long-term predictions for cloud resource allocation.

Cloud computing depends on the dynamic allocation and release of resources, on demand, to meet heterogeneous computing needs. This is challenging for cloud data centers, which process huge amounts of data characterised by its high volume, velocity, variety and veracity (4Vs model). Managing such a workload is increasingly difficult using state-of-the-art methods for monitoring and adaptation, which typically react to service failures after the fact. To address this, we seek to develop proactive methods for predicting future resource exhaustion and cloud service failures. Our work uses a realistic test bed in the cloud, which is instrumented to monitor and analyze resource usage. In this article, we employed the optimal Kalman filtering technique to build a predictive and analytic framework for cloud server KPIs, based on historical data. Our$k$-step-ahead predictions on historical data yielded a prediction accuracy of 95.59%. The information generated from the framework can best be used for optimal resources provisioning, admission control and cloud SLA management.

Summary The Stream X‐Machine (SXM) testing method provides strong and repeatable guarantees of functional correctness, up to a specification. These qualities make the method attractive for software certification, especially in the domain of brokered cloud services, where arbitrage seeks to substitute functionally equivalent services from alternative providers. However, practical obstacles include the difficulty in providing a correct specification, the translation of abstract paths into feasible concrete tests and the large size of generated test suites. We describe a novel SXM verification and testing method, which automatically checks specifications for completeness and determinism, prior to generating complete test suites with full grounding information. Three optimization steps achieve up to a 10‐fold reduction in the size of the test suite, removing infeasible and redundant tests. The method is backed by a set of tools to validate and verify the SXM specification, generate technology‐agnostic test suites and ground these in SOAP, REST or rich‐client service implementations. The method was initially validated using seven specifications, three cloud platforms and five grounding strategies.

© 2018 International Speech Communication Association. All rights reserved. In speech pathology, new assistive technologies using ASR and machine learning approaches are being developed for detecting speech disorder events. Classically-trained ASR model tends to remove disfluencies from spoken utterances, due to its focus on producing clean and readable text output. However, diagnostic systems need to be able to track speech disfluencies, such as stuttering events, in order to determine the severity level of stuttering. To achieve this, ASR systems must be adapted to recognise full verbatim utterances, including pseudo-words and non-meaningful part-words. This work proposes a training regime to address this problem, and preserve a full verbatim output of stuttering speech. We use a lightly-supervised approach using task-oriented lattices to recognise the stuttering speech of children performing a standard reading task. This approach improved the WER by 27.8% relative to a baseline that uses word-lattices generated from the original prompt. The improved results preserved 63% of stuttering events (including sound, word, part-word and phrase repetition, and revision). This work also proposes a separate correction layer on top of the ASR that detects prolongation events (which are poorly recog-nised by the ASR). This increases the percentage of preserved stuttering events to 70%.

Cloud application platforms gain popularity and have the potential to alter the way service based cloud applications are developed involving utilisation of platform basic services. A platform basic service is considered as a piece of software, which provides certain functionality and is usually offered via a web API, e.g. e-mail, payment, authentication service. However, the proliferation and diversification of platform basic services and the available providers increase the challenge for the application developers to integrate them and deal with the heterogeneous providers' web APIs. Therefore, a new approach of developing applications should be adopted in which developers leverage multiple platform basic services independently from the target application platforms. To this end, this paper presents a development framework whose objective is to enable the consistent integration of the platform services, and to allow the seamless use of the concrete providers by alleviating the heterogeneities among them.

Almost all organisations, irrespective of their size, rely on IT systems. Individuals are making increasing use of web applications for personal and social activities. Furthermore, as systems in general become more technologically advanced, an increasing proportion of the implementation is in software rather than in hardware. As a result, the quality and reliability of systems and the data they contain is increasingly dependent on the correctness, robustness and security of the associated software. The trend is clear: software is becoming pervasive and, as a result, software quality assurance, in the widest sense, is becoming a crucial everyday concern for all of us. Software testing is, by far, the most important means of assuring software quality. The Testing: Academic and Industrial Conference-Practice and Research Techniques 2010 (TAIC-PART 2010) was the fifth conference in a series of highly successful events. The conference brought together industrialists and academics to promote collaboration on problems in software testing. Among the wide range of topics in computer science and software engineering, software testing is an ideal candidate for academic and industrial collaboration because advances in research can have such wide-ranging and far-reaching implications for industry. Conversely, the advances in computing and communications technology and the growth of the associated software engineering activity are producing new research challenges at an increasing rate. TAIC-PART 2010 received forty paper submissions. After a rigorous reviewing process in which each paper was subjected to at least three independent reviews followed by programme committee discussion, 15 full papers and seven abstracts were accepted. One of these submissions was from industry, four were from collaborations between industry and academia and the remainder came from academia. The papers originated from 13 countries in Europe, North and South America, and Asia. The two best papers selected by the editors for publication in this special section are significantly extended versions of those TAIC-PART papers that received the strongest support from the referees. Each of the extended papers has been refereed by at least three expert reviewers and has undergone revisions as a result. They not only address important issues in security testing and fault localisation but also use novel and interesting techniques that could be deployed more widely. The first paper by Ben Kam and Thomas Dean, Linguistic security testing for text communication protocols considers the problem of protocol security testing. Protocols rely heavily on syntactic structures, and the authors adopt a syntax-based approach to security testing. The basic idea is to capture existing input to the application under test. The input is then parsed into an abstract syntax tree. Using a set of rewrite rules, the input is transformed to create test cases. A key contribution of their work is that they use a relatively simple context-free grammar together with XML mark-up rules to specify lexical, syntactic and context sensitive constraints. From this augmented grammar, they automatically generate a program that inserts XML tags into the appropriate parts of the captured test input. Multiple test generators can then use the tagged test input to produce different test cases, which are used to validate the security protocols of the software under test. In a case study, the authors tested an application that uses the iCalendar protocol for the communication of calendar information. Just over a thousand test cases were generated from a sample input; and upon execution of those test cases, an error was found, corresponding to a weakness in the iCalendar security protocol. Overall, the Syntax-based Security Testing framework is a relatively lightweight way of producing a protocol independent testing tool. New testing schemes can be accommodated by augmenting the grammar to add additional mark-up and creating the appropriate test case generators. The second paper by Mihai Nica, Simona Nica and Franz Wotawa, On the use of mutations and testing for debugging, is concerned with the problem of debugging but from a testing viewpoint. The main goal is to use testing as a way of localising faults within software. The underlying concept is to treat the statements in a program as a set of equations on the variables that appear in those statements. Each test case constrains the values of input and output variables. A failing test produces an inconsistency in the system of equations. The fault localisation process works by attempting to restore consistency. By removing individual statements until the system of equations is consistent, it is possible to identify candidate faulty statements, of which there are more than the actual number of faults. These candidates are subject to mutation, as a way of attempting to effect a repair. Candidates are retained if their repaired mutation causes an increase in consistency, whereas other candidates are discarded. Further test cases may be introduced to compare alternative surviving candidates and alternative repairs, which may need some input from the tester, or an oracle to help localise the fault. The approach is largely automated, but there are limitations as to the program constructs that can be represented as a set of equations. In addition, the constraint solver does not scale to large programs. In spite of these limitations, however, the approach is applied to a real world program with encouraging results. Both papers have a strong technical and also an empirical content. They capture the interplay of research and practice that is central to the aim of the conference and are fine examples of research in software testing. Finally, we would like to thank all the authors and reviewers for their effort and time. Furthermore, we would also like to thank the editors-in-chief of Software: Practice and Experience, Nigel Horspool and Andy Wellings, and the editorial staff for their support.

Abstract Despite being widely known and accepted in industry, the Z formal specification language has not so far been well supported by automated verification tools, mostly because of the challenges in handling the abstraction of the language. In this paper we discuss a novel approach to building a model-checker for Z, which involves implementing a translation from Z into SAL, the input language for the Symbolic Analysis Laboratory, a toolset which includes a number of model-checkers and a simulator. The Z2SAL translation deals with a number of important issues, including: mapping unbounded, abstract specifications into bounded, finite models amenable to a BDD-based symbolic checker; converting a non-constructive and piecemeal style of functional specification into a deterministic, automaton-based style of specification; and supporting the rich set-based vocabulary of the Z mathematical toolkit. This paper discusses progress made towards implementing as complete and faithful a translation as possible, while highlighting certain assumptions, respecting certain limitations and making use of available optimisations. The translation is illustrated throughout with examples; and a complete working example is presented, together with performance data.

Many design notations are used during software development to help the developers better understand the required system. However they are infrequently shown to clients, partly because developers believe that clients don't understand them. In this study we investigate the extent to which clients comprehend three types of diagram. Two popular UML diagrams (activity and use case) and Extreme X-Machines diagrams (a type of state diagram developed to support Extreme Programming) were shown to three clients for whom we had recently delivered the software that was represented. The clients were given some simple guidance on interpreting them and asked to evaluate how well they understood them. This pilot study found that all the diagrams studied seemed to be equally well understood, but further studies are required to evaluate their usefulness.

Abstract A behavioural theory of object compatibility is presented, which has implications for object‐oriented regression testing. The theory predicts that only certain models of state refinement yield compatible types, dictating the legitimate design styles to be adopted in object statecharts. The theory also predicts that conformity‐testing using regression tests is inadequate. Functionally complete test‐sets that are applied as regression tests to subtype objects are usually expected to cover the functionality of the original type, even though they are clearly not expected to cover extra functionality introduced in the subtype. However, such regression testing is proven to cover strictly less than the original state‐space in the new context and so provides much weaker confidence than expected. A different retesting model is proposed, based on full automatic test regeneration from the subtype's specification. This method can guarantee equivalent levels of confidence after retesting. The behavioural conformity desired by regression testing can then be proven by verification in the theory. Copyright © 2006 John Wiley & Sons, Ltd.

Dynamic memory management in C++ is complex, especially across the boundaries of library abstract data types. C++ libraries designed in the orthodox canonical form (OCF) alleviate some of the problems by ensuring that classes which manage any kind of heap structures faithfully copy and delete these. However, in certain common circumstances, OCF heap structures are wastefully copied multiple times. General reference counting is not an option in OCF, since a shared body violates the intended value semantics; although a copy-on-write policy can be made to work with borrowed heap structures. A simpler ownership policy, based on larceny, allows low-level memory manager objects to steal heap structures from temporary variables, in properly isolated circumstances. Various strategies for regulating theft are presented, ranging from pilfer-constructors to locks on heap data. Larceny has similarities with other transfer of ownership patterns, but is more a core implementation technique designed to improve the efficiency and effectiveness of OCF-conformant libraries.