Software quality assurance aims to ensure that the software product meets the quality standards expected by the customer. This special issue of Software Tools for Technology Transfer is concerned with the foundations on which software quality assurance is built. It introduces the papers that focus on this topic and that have been selected from the 20th International Conference on Fundamental Approaches to Software Engineering (FASE’17)

Huisman, Marieke

Rubin, Julia

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International Journal on Software Tools for Technology Transfer (2018) 20:611–613https://doi.org/10.1007/s10009-018-0504-7INTRODUCTIONSoftware quality tools and techniques presented in FASE’17Marieke Huisman1 · Julia Rubin2Published online: 3 September 2018© The Author(s) 2018AbstractSoftware quality assurance aims to ensure that the software product meets the quality standards expected by the customer.This special issue of Software Tools for Technology Transfer is concerned with the foundations on which software qualityassurance is built. It introduces the papers that focus on this topic and that have been selected from the 20th InternationalConference on Fundamental Approaches to Software Engineering (FASE’17).Keywords Software engineering · Software foundations · Software quality1 IntroductionThis special issue of the journal Software Tools for Technol-ogy Transfer (STTT) contains revised and extended versionsof six papers selected out of 25 papers presented at the20th International Conference on Fundamental Approachesto Software Engineering (FASE’17) [4].FASE is concerned with the foundations on which soft-ware engineering is built. Papers published at FASE describecontributions that make software engineering a more matureand soundly based discipline and are supported by appropri-ate arguments and validation. Topics that are of interest toFASE are:– Software engineering as an engineering discipline,including its interaction with and impact on society;– Requirements engineering: capture, consistency, andchange management of software requirements;– Software architectures: description and analysis of thearchitecture of individual systems or classes of applica-tions;– Specification, design, and implementation of particularclasses of systems: adaptive, collaborative, embedded,distributed, mobile, pervasive, or service-oriented appli-cations;B Marieke HuismanMarieke.Huisman@ewi.utwente.nlJulia Rubinmjulia@ece.ubc.ca1 University of Twente, Enschede, The Netherlands2 University of British Columbia, Vancouver, Canada– Software quality: validation and verification of softwareusing theoremproving,model checking, testing, analysis,refinement methods, metrics or visualization techniques;– Model-driven development and model transformation:meta-modeling, design and semantics of domain-specificlanguages, consistency and transformation of models,generative architectures;– Software processes: support for iterative, agile, and opensource development;– Software evolution: refactoring, reverse andre-engineering, configuration management and architec-tural change, or aspect-orientation.The peer-reviewed papers collected in this special issuehave been invited by the guest editors among the top paperspresented at FASE’17 based on their relevance to STTT.Theyall report on advances in tools and techniques for softwarequality assurance.The selected papers cover two domains: tools for systemdevelopers and techniques for enhancing quality-assurancetools. In the first domain, there are three papers that aim athelping developers to improve the quality of their systems.They are discussed in Sect. 2. In the second domain, there arethree papers that describe techniques aiming to build foun-dations for new quality assurance tools. They are discussedin Sect. 3.2 Tools for system developersVarious techniques to help developers to improve the qualityof their systems are discussed: reducing system complexity123612 M. Huisman, J. Rubinby splitting large CPS models into smaller ones, verifica-tion of model transformations in model-driven engineering,and automatic generation of workarounds to bypass systemfailures.Tactical contract composition for hybrid systemcomponent verification: Müller et al. [5]This paper presents an approach for verifying component-based models for cyber-physical systems (CPSs) with dis-crete and continuous dynamics. Examples for systems inthe first category are autopilots in airplanes and con-trollers in self-driving cars, whereas examples of systemsin the second category are motion of airplanes or cars.Componentization—splitting large models of CPSs intomultiple smaller pieces with local responsibilities—reducessystem complexity but introduces challenges for verification.That is because both local component behavior, e.g., deci-sions andmotion of a robot, and inherently global phenomenae.g., time, co-occur, as components can interact virtually, e.g.,robots communicate, and physically, e.g., a robot manipu-lates an object.In order to verify each component in isolation, this paperintroduces component contracts—input assumptions andoutput guarantees for each component—which emphasizethe externally observable nature of component behavior. Thatis, the contracts specify themagnitude of change between twoobservations, e.g., current speed is at most twice the previ-ously observed speed, and also capture the rate of changee.g., current speed is at most previous speed increased byaccelerating for some time. The contracts abstract the hybrid,continuous-time behavior of one component to discrete-time observations available to other components. Given thedefinition of contracts, the paper proves that the safety ofcompatible components implies safety of the composed sys-tem, facilitating compositional verification of CPSs.Slicing ATLmodel transformations for scalabledeductive verification and fault localization: Chengand Tisi [2]This paper introduces an approach for verifying the correct-ness of model transformations, which are used in Model-Driven Engineering (MDE) for manipulating, evolving,and translating models, up to code generation. Correctnessrequirements for a model transformation are encoded astransformation contracts. A model transformation that doesnot satisfy the contract would generate faulty models, whoseeffect could be unpredictably propagated into subsequentMDE steps and compromise the reliability of the whole soft-ware development process.The paper specifically focuses on ATL models. The rela-tional nature of ATL is exploited to deduce static traceinformation, i.e., mappings among types of generated ele-ments and rules that potentially generate these types. Thetrace information is then used to decompose the modeltransformation contract, and, for each sub-contract, slicethe transformation to the only rules that may be responsi-ble for fulfilling that sub-contract. Slicing makes it possibleto devise a highly scalable technique for verifying largemodel transformations against a large number of contracts.To demonstrate industry-readiness, the technique is imple-mented as an extension to VeriATL verification system.Automated workarounds from Java programspecifications based on SAT solving: Uva et al. [7]This paper presents an approach for automatically generatingworkarounds that bypass failures induced by bugs in Javaprograms. A workaround is an alternative routine that thesystem offers once the primary routine fails. Workaroundscan be used in place of the failing routines, to circumventfailures.Unlike existing approaches to workaround-based systemrecovery, which consider workarounds that are producedfrom user-provided abstract models or equivalent sequencesof operations provided directly by the user, this paper com-putes workarounds from Java code equipped with formalspecifications. Such approach makes it possible to con-sider the particular state where the failure occurred, leadingto repairs that are more accurate and state-specific. Thepaper shows that the proposed technique is able to producerepairs in several cases where previous workaround-basedapproaches are inapplicable.3 Techniques for enhancing qualityassurance toolsAlso various techniques are presented that extend andimprove existing quality assurance tools, such as an improve-ment in model learning for probabilistic models, automatedreasoning for graph-based modeling, and synthesis of pro-gram slicers from algebraic specifications.Learning probabilistic models for model checking:an evolutionary approach and an empiricalstudy—Wang et al. [8]This paper focuses on the problem of obtaining a systemmodel used for analysis by several automated techniques,such as model checking and model-based testing. Systemmodeling is often done manually, which hinder the adoptionofmodel-based systemanalysis anddevelopment techniques.To overcome this problem, existing approaches “learn” mod-els based on sample system executions.123Software quality tools and techniques presented in FASE’17 613This paper is concerned with the question of how muchone can generalize from the observed samples and how fastwould learning converge. It proposes a new approach to con-trol the degree of generalization and shows that the approachconverges faster and learns models which are much smallerthan those learned by existing approaches while providingbetter or similar analysis results. The paper also systemat-ically analyzes existing learning approaches, showing thatlearningmodels for model checkingmight not be as effectiveas the estimation obtained by sampling many system execu-tions within the same amount of time. Finally, the authorsinvestigate how well current abstraction techniques for prob-abilistic model learning scale, showing that it can only workfor a limited class of properties. The papermakes suggestionsfor future research on the topic.Automated reasoning for attributed graphproperties: Schneider et al. [6]This paper focuses on attributed graphs: graphs that areequipped with attributes to express additional informationabout nodes and edges, such as names of entities or weightsof relationships. Such graphs are often used in model-basedengineering, in the formal analysis and verification of sys-tems where the states are modeled as graphs, in the formalmodeling and analysis of sets of semi-structured documents,especially if they are related by links, or of graph queries inthe graph database domain.The authors introduce a new logic for expressing proper-ties on attributed graphs, which is expected to strengtheningmodeling capabilities and enable automated analysis. In thislogic, graph and attribution parts are separated. The graphpart is equivalent to first-order logic on graphs as intro-duced by Courcelle [3]. The attribution part is added to thegraph part by reverting to the symbolic approach to graphattribution, where attributes are represented symbolically byvariables whose possible values are specified by a set of con-straints making use of algebraic specifications. The authorsdemonstrate the applicability of the proposed approach forgraph database query validation.Slicing from formal semantics: Asavoae et al. [1]This paper describes a tool that synthesizes a program slicerfrom a given algebraic specification of a programming lan-guage operational semantics. The diversity of programminglanguages and the variety of definitions for program slicesmotivates the development of generic slicing techniques. Thetool introduced in this work addresses this problem. Morespecifically, it takes as input a programming language seman-tics and synthesizes the necessary ingredients for programslicing, i.e., as data- and control-flow features of the lan-guages. Then, it takes a program and, based on the flowfeatures of the language, produces the program slice withrespect to a slicing criterion. The authors show how the pro-duced slicing tool can be used for validation of embeddedsystem designs.Acknowledgements We are grateful to all the authors for their con-tributions and to the program committee of FASE’17 for their help forselecting the conference program, including the papers for this issue.Weare especially grateful to the FASE’17 program committee and externalreferees who reviewed the extended version of the papers that appearin this special issue.Open Access This article is distributed under the terms of the CreativeCommons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,and reproduction in any medium, provided you give appropriate creditto the original author(s) and the source, provide a link to the CreativeCommons license, and indicate if changes were made.References1. Asavoae, I.M., Asavoae, M., Riesco, A.: Slicing from formalsemantics: Chisel—a tool for generic program slicing. Int J SoftwTools Technol Transfer (2018). https://doi.org/10.1007/s10009-018-0500-y2. Cheng, Z., Tisi, M.: Slicing ATLmodel transformations for scalabledeductive verification and fault localization. Int J Softw Tools Tech-nol Transfer (2018). https://doi.org/10.1007/s10009-018-0491-83. Courcelle, B.: The expression of graph properties and graph trans-formations in monadic second-order logic. In: Grzegorz, R. (ed.)Handbook of Graph Grammars and Computing by Graph Transfor-mation, pp. 313–400. World Scientific Publishing Co., Inc, RiverEdge (1997)4. Huisman, M., Rubin, J. (ed.): Fundamental Approaches to SoftwareEngineering: 20th International Conference, FASE 2017, Held asPart of the European Joint Conferences on Theory and Practice ofSoftware, ETAPS 2017, Uppsala, Sweden, 22–29 April 2017, Pro-ceedings, Volume 10202 of Lecture Notes in Computer Science.Springer, Berlin (2017)5. Müller, A., Mitsch, S., Retschitzegger, W., Schwinger, W., Platzer,A.: Tactical contract composition for hybrid system component ver-ification. Int J Softw Tools Technol Transfer (2018). https://doi.org/10.1007/s10009-018-0502-96. Schneider, S., Lambers, L., Orejas, F.: Automated reasoning forattributed graph properties. Int J Softw Tools Technol Transfer(2018). https://doi.org/10.1007/s10009-018-0496-37. Uva, M., Ponzio, P., Regis, G., Aguirre, N., Frias, M.F.: Automatedworkarounds from Java program specifications based on SAT solv-ing. Int J Softw Tools Technol Transfer (2018). https://doi.org/10.1007/s10009-018-0503-88. Wang, J., Sun, J., Yuan, Q., Pang., J.: Learning probabilistic mod-els for model checking: an evolutionary approach and an empiricalstudy. Int J Softw Tools Technol Transfer (2018). https://doi.org/10.1007/s10009-018-0492-7123

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