Engineering model transformations with transML

The final publication is available at Springer via http://dx.doi.org/10.1007%2Fs10270-011-0211-2Model transformation is one of the pillars of model-driven engineering (MDE). The increasing complexity of systems and modelling languages has dramatically raised the complexity and size of model transformations as well. Even though many transformation languages and tools have been proposed in the last few years, most of them are directed to the implementation phase of transformation development. In this way, even though transformations should be built using sound engineering principles—just like any other kind of software—there is currently a lack of cohesive support for the other phases of the transformation development, like requirements, analysis, design and testing. In this paper, we propose a unified family of languages to cover the life cycle of transformation development enabling the engineering of transformations. Moreover, following an MDE approach, we provide tools to partially automate the progressive refinement of models between the different phases and the generation of code for several transformation implementation languages.This work has been sponsored by the Spanish Ministry of Science and Innovation with project METEORIC (TIN2008-02081), and by the R&D program of the Community of Madrid with projects “e-Madrid" (S2009/TIC-1650). Parts of this work were done during the research stays of Esther and Juan at the University of York, with financial support from the Spanish Ministry of Science and Innovation (grant refs. JC2009-00015, PR2009-0019 and PR2008-0185)

Using active database for management of requirements change

Software system development projects experience numerous changes during their life cycle. These changes are inevitable and driven by several factors including changes to a system\u27s environment and changes of customers\u27 needs. Requirements change has been reported as the major contributing factor for poor quality or even failures of software projects. This indicates that management of requirements change still remains a challenging problem in software development. A critical part of the requirements change management process is impact analysis. To carry out impact assessment, traceability information is needed. Over two decades, requirements traceability has been an important research topic in software research, but the actual practice of maintaining traceability information is not always entirely successful. In this thesis, a new traceability technique was presented for mapping dynamic behaviors of requirements into Active Databases. The technique keeps requirements and their related artifacts synchronized with respect to their states. It automatically maintains traceability links between requirements and related artifacts when a requirement is changed. This approach can not only efficiently handle basic and necessary traceability functions, but also centralize reactive behavior by using Active Database to ensure no one bypass traceability policies.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .G42. Source: Masters Abstracts International, Volume: 44-03, page: 1401. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Traceability of Requirements and Software Architecture for Change Management

At the present day, software systems get more and more complex. The requirements of software systems change continuously and new requirements emerge frequently. New and/or modified requirements are integrated with the existing ones, and adaptations to the architecture and source code of the system are made. The process of integration of the new/modified requirements and adaptations to the software system is called change management. The size and complexity of software systems make change management costly and time consuming. To reduce the cost of changes, it is important to apply change management as early as possible in the software development cycle. Requirements traceability is considered crucial in change management for establishing and maintaining consistency between software development artifacts. It is the ability to link requirements back to stakeholders’ rationales and forward to corresponding design artifacts, code, and test cases. When changes for the requirements of the software system are proposed, the impact of these changes on other requirements, design elements and source code should be traced in order to determine parts of the software system to be changed. Determining the impact of changes on the parts of development artifacts is called change impact analysis. Change impact analysis is applicable to many development artifacts like requirements documents, detailed design, source code and test cases. Our focus is change impact analysis in requirements and software architecture. The need for change impact analysis is observed in both requirements and software architecture. When a change is introduced to a requirement, the requirements engineer needs to find out if any other requirement related to the changed requirement is impacted. After determining the impacted requirements, the software architect needs to identify the impacted architectural elements by tracing the changed requirements to software architecture. It is hard, expensive and error prone to manually trace impacted requirements and architectural elements from the changed requirements. There are tools and approaches that automate change impact analysis like IBM Rational RequisitePro and DOORS. In most of these tools, traces are just simple relations and their semantics is not considered. Due to the lack of semantics of traces in these tools, all requirements and architectural elements directly or indirectly traced from the changed requirement are candidate impacted. The requirements engineer has to inspect all these candidate impacted requirements and architectural elements to identify changes if there are any. In this thesis we address the following problems which arise in performing change impact analysis for requirements and software architecture. Explosion of impacts in requirements after a change in requirements. In practice, requirements documents are often textual artifacts with implicit structure. Most of the relations among requirements are not given explicitly. There is a lack of precise definition of relations among requirements in most tools and approaches. Due to the lack of semantics of requirements relations, change impact analysis may produce high number of false positive and false negative impacted requirements. A requirements engineer may have to analyze all requirements in the requirements document for a single change. This may result in neglecting the actual impact of a change. Manual, expensive and error prone trace establishment. Considerable research has been devoted to relating requirements and design artifacts with source code. Less attention has been paid to relating Requirements (R) with Architecture (A) by using well-defined semantics of traces. Designing architecture based on requirements is a problem solving process that relies on human experience and creativity, and is mainly manual. The software architect may need to manually assign traces between R&A. Manual trace assignment is time-consuming, expensive and error prone. The assigned traces might be incomplete and invalid. Explosion of impacts in software architecture after a change in requirements. Due to the lack of semantics of traces between R&A, change impact analysis may produce high number of false positive and false negative impacted architectural elements. A software architect may have to analyze all architectural elements in the architecture for a single requirements change. In this thesis we propose an approach that reduces the explosion of impacts in R&A. The approach employs semantic information of traces and is supported by tools. We consider that every relation between software development artifacts or between elements in these artifacts can play the role of a trace for a certain traceability purpose like change impact analysis. We choose Model Driven Engineering (MDE) as a solution platform for our approach. MDE provides a uniform treatment of software artifacts (e.g. requirements documents, software design and test documents) as models. It also enables using different formalisms to reason about development artifacts described as models. To give an explicit structure to requirements documents and treat requirements, architecture and traces in a uniform way, we use metamodels and models with formally defined semantics. The thesis provides the following contributions: A modeling language for definition of requirements models with formal semantics. The language is defined according to the MDE principles by defining a metamodel. It is based on a survey about the most commonly found requirements types and relation types. With this language, the requirements engineer can explicitly specify the requirements and the relations among them. The semantics of these entities is given in First Order Logic (FOL) and allows two activities. First, new relations among requirements can be inferred from the initial set of relations. Second, requirements models can be automatically checked for consistency of the relations. Tool for Requirements Inferencing and Consistency Checking (TRIC) is developed to support both activities. The defined semantics is used in a technique for change impact analysis in requirements models. A change impact analysis technique for requirements using semantics of requirements relations and requirements change types. The technique aims at solving the problem of explosion of impacts in requirements when semantics of requirements relations is missing. The technique uses formal semantics of requirements relations and requirements change types. A classification of requirements changes based on the structure of a textual requirement is given and formalized. The semantics of requirements change types is based on FOL. We support three activities for impact analysis. First, the requirements engineer proposes changes according to the change classification before implementing the actual changes. Second, the requirements engineer indentifies the propagation of the changes to related requirements. The change alternatives in the propagation are determined based on the semantics of change types and requirements relations. Third, possible contradicting changes are identified. We extend TRIC with a support for these activities. The tool automatically determines the change propagation paths, checks the consistency of the changes, and suggests alternatives for implementing the change. A technique that provides trace establishment between R&A by using architecture verification and semantics of traces. It is hard, expensive and error prone to manually establish traces between R&A. We present an approach that provides trace establishment by using architecture verification together with semantics of requirements relations and traces. We use a trace metamodel with commonly used trace types. The semantics of traces is formalized in FOL. Software architectures are expressed in the Architecture Analysis and Design Language (AADL). AADL is provided with a formal semantics expressed in Maude. The Maude tool set allows simulation and verification of architectures. The first way to establish traces is to use architecture verification techniques. A given requirement is reformulated as a property in terms of the architecture. The architecture is executed and a state space is produced. This execution simulates the behavior of the system on the architectural level. The property derived from the requirement is checked by the Maude model checker. Traces are generated between the requirement and the architectural components used in the verification of the property. The second way to establish traces is to use the requirements relations together with the semantics of traces. Requirements relations are reflected in the connections among the traced architectural elements based on the semantics of traces. Therefore, new traces are inferred from existing traces by using requirements relations. We use semantics of requirements relations and traces to both generate/validate traces and generate/validate requirements relations. There is a tool support for our approach. The tool provides the following: (1) generation/validation of traces by using requirements relations and/or verification of architecture, (2) generation/validation of requirements relations by using traces. A change impact analysis technique for software architecture using architecture verification and semantics of traces between R&A. The software architect needs to identify the impacted architectural elements after requirements change. We present a change impact analysis technique for software architecture using architecture verification and semantics of traces. The technique is semi-automatic and requires participation of the software architect. Our technique has two parts. The first part is to identify the architectural elements that implement the system properties to which proposed requirements changes are introduced. By having the formal semantics of requirements relations and traces, we identify which parts of software architecture are impacted by a proposed change in requirements. We have extended TRIC for determining candidate impacted architectural elements. The second part of our technique is to propose possible changes for software architecture when the software architecture does not satisfy the new and/or changed requirements. The technique is based on architecture verification. The output of verification is a counter example if the requirements are not satisfied. The counter example is used with a classification of architectural changes in order to propose changes in the software architecture. These changes produce a new version of the architecture that possibly satisfies the new or the changed requirements

pecification of dependency areas in UML designs

A concept of dependency areas can help in tracing an impact of artifacts of a project (requirements, elements of the UML design, extracts of the code) and assist in their evolution. The dependency area of an element of a UML design is a part of the design that is highly influenced by the given initial element. Dependency areas are identified using sets of propagation rules and strategies. Selection strategies control application of many, possible rules. Bounding strategies limit the number of elements assigned to the areas. This paper is devoted to the specification of the rules and strategies. They are specified using an extended UML meta-model and expressions in the Object Constraint Language (OCL)

An approach to impact analysis in software maintenance

Impact analysis is a software maintenance activity, which consists of determining the scope of a requested change, as a basis for planning and implementing it. After a change request has been specified (change understanding) and the initial part of the system to be changed has been identified (change localization), impact analysis helps to understand consequences of the change on other parts of the system. Induced changes, also named ripple effects, among software components are detected. Most existing approaches perform impact analysis for changes occurring at the code level. In this thesis, concepts developed to perform impact analysis at the code level are applied to trace changes occurring at the design level. The method consists of proposing an activity model addressing the different steps of impact analysis and a data model on which propagations of changes can be traced. The method is validated with a case study applied to a system from the aerospace field. The tools we developed on PCTE help for consistency checks in HOOD based designs during editing. Our data-model based on an Entity Relationship notation describes a way to model HOOD diagrams in PCTE and further on to propagate changes on the repository. Examples chosen address the design phase of a simple engine system. We show that addressing modifications at a higher level of abstraction than the code eases understanding and localization of changes. It also limits the propagation of ripple effects (i.e., unexpected behaviour of the system) by detecting secondary changes at an earlier stage

Design of a Machine Learning-based Approach for Fragment Retrieval on Models

[ES] El aprendizaje automático (ML por sus siglas en inglés) es conocido como la rama de la inteligencia artificial que reúne algoritmos estadísticos, probabilísticos y de optimización, que aprenden empíricamente. ML puede aprovechar el conocimiento y la experiencia que se han generado durante años en las empresas para realizar automáticamente diferentes procesos. Por lo tanto, ML se ha aplicado a diversas áreas de investigación, que estudian desde la medicina hasta la ingeniería del software. De hecho, en el campo de la ingeniería del software, el mantenimiento y la evolución de un sistema abarca hasta un 80% de la vida útil del sistema. Las empresas, que se han dedicado al desarrollo de sistemas software durante muchos años, han acumulado grandes cantidades de conocimiento y experiencia. Por lo tanto, ML resulta una solución atractiva para reducir sus costos de mantenimiento aprovechando los recursos acumulados. Específicamente, la Recuperación de Enlaces de Trazabilidad, la Localización de Errores y la Ubicación de Características se encuentran entre las tareas más comunes y relevantes para realizar el mantenimiento de productos software. Para abordar estas tareas, los investigadores han propuesto diferentes enfoques. Sin embargo, la mayoría de las investigaciones se centran en métodos tradicionales, como la indexación semántica latente, que no explota los recursos recopilados. Además, la mayoría de las investigaciones se enfocan en el código, descuidando otros artefactos de software como son los modelos. En esta tesis, presentamos un enfoque basado en ML para la recuperación de fragmentos en modelos (FRAME). El objetivo de este enfoque es recuperar el fragmento del modelo que realiza mejor una consulta específica. Esto permite a los ingenieros recuperar el fragmento que necesita ser trazado, reparado o ubicado para el mantenimiento del software. Específicamente, FRAME combina la computación evolutiva y las técnicas ML. En FRAME, un algoritmo evolutivo es guiado por ML para extraer de manera eficaz distintos fragmentos de un modelo. Estos fragmentos son posteriormente evaluados mediante técnicas ML. Para aprender a evaluarlos, las técnicas ML aprovechan el conocimiento (fragmentos recuperados de modelos) y la experiencia que las empresas han generado durante años. Basándose en lo aprendido, las técnicas ML determinan qué fragmento del modelo realiza mejor una consulta. Sin embargo, la mayoría de las técnicas ML no pueden entender los fragmentos de los modelos. Por lo tanto, antes de aplicar las técnicas ML, el enfoque propuesto codifica los fragmentos a través de una codificación ontológica y evolutiva. En resumen, FRAME está diseñado para extraer fragmentos de un modelo, codificarlos y evaluar cuál realiza mejor una consulta específica. El enfoque ha sido evaluado a partir de un caso real proporcionado por nuestro socio industrial (CAF, un proveedor internacional de soluciones ferroviarias). Además, sus resultados han sido comparados con los resultados de los enfoques más comunes y recientes. Los resultados muestran que FRAME obtuvo los mejores resultados para la mayoría de los indicadores de rendimiento, proporcionando un valor medio de precisión igual a 59.91%, un valor medio de exhaustividad igual a 78.95%, una valor-F medio igual a 62.50% y un MCC (Coeficiente de Correlación Matthews) medio igual a 0.64. Aprovechando los fragmentos recuperados de los modelos, FRAME es menos sensible al conocimiento tácito y al desajuste de vocabulario que los enfoques basados en información semántica. Sin embargo, FRAME está limitado por la disponibilidad de fragmentos recuperados para llevar a cabo el aprendizaje automático. Esta tesis presenta una discusión más amplia de estos aspectos así como el análisis estadístico de los resultados, que evalúa la magnitud de la mejora en comparación con los otros enfoques.[CAT] L'aprenentatge automàtic (ML per les seues sigles en anglés) és conegut com la branca de la intel·ligència artificial que reuneix algorismes estadístics, probabilístics i d'optimització, que aprenen empíricament. ML pot aprofitar el coneixement i l'experiència que s'han generat durant anys en les empreses per a realitzar automàticament diferents processos. Per tant, ML s'ha aplicat a diverses àrees d'investigació, que estudien des de la medicina fins a l'enginyeria del programari. De fet, en el camp de l'enginyeria del programari, el manteniment i l'evolució d'un sistema abasta fins a un 80% de la vida útil del sistema. Les empreses, que s'han dedicat al desenvolupament de sistemes programari durant molts anys, han acumulat grans quantitats de coneixement i experiència. Per tant, ML resulta una solució atractiva per a reduir els seus costos de manteniment aprofitant els recursos acumulats. Específicament, la Recuperació d'Enllaços de Traçabilitat, la Localització d'Errors i la Ubicació de Característiques es troben entre les tasques més comunes i rellevants per a realitzar el manteniment de productes programari. Per a abordar aquestes tasques, els investigadors han proposat diferents enfocaments. No obstant això, la majoria de les investigacions se centren en mètodes tradicionals, com la indexació semàntica latent, que no explota els recursos recopilats. A més, la majoria de les investigacions s'enfoquen en el codi, descurant altres artefactes de programari com són els models. En aquesta tesi, presentem un enfocament basat en ML per a la recuperació de fragments en models (FRAME). L'objectiu d'aquest enfocament és recuperar el fragment del model que realitza millor una consulta específica. Això permet als enginyers recuperar el fragment que necessita ser traçat, reparat o situat per al manteniment del programari. Específicament, FRAME combina la computació evolutiva i les tècniques ML. En FRAME, un algorisme evolutiu és guiat per ML per a extraure de manera eficaç diferents fragments d'un model. Aquests fragments són posteriorment avaluats mitjançant tècniques ML. Per a aprendre a avaluar-los, les tècniques ML aprofiten el coneixement (fragments recuperats de models) i l'experiència que les empreses han generat durant anys. Basant-se en l'aprés, les tècniques ML determinen quin fragment del model realitza millor una consulta. No obstant això, la majoria de les tècniques ML no poden entendre els fragments dels models. Per tant, abans d'aplicar les tècniques ML, l'enfocament proposat codifica els fragments a través d'una codificació ontològica i evolutiva. En resum, FRAME està dissenyat per a extraure fragments d'un model, codificar-los i avaluar quin realitza millor una consulta específica. L'enfocament ha sigut avaluat a partir d'un cas real proporcionat pel nostre soci industrial (CAF, un proveïdor internacional de solucions ferroviàries). A més, els seus resultats han sigut comparats amb els resultats dels enfocaments més comuns i recents. Els resultats mostren que FRAME va obtindre els millors resultats per a la majoria dels indicadors de rendiment, proporcionant un valor mitjà de precisió igual a 59.91%, un valor mitjà d'exhaustivitat igual a 78.95%, una valor-F mig igual a 62.50% i un MCC (Coeficient de Correlació Matthews) mig igual a 0.64. Aprofitant els fragments recuperats dels models, FRAME és menys sensible al coneixement tàcit i al desajustament de vocabulari que els enfocaments basats en informació semàntica. No obstant això, FRAME està limitat per la disponibilitat de fragments recuperats per a dur a terme l'aprenentatge automàtic. Aquesta tesi presenta una discussió més àmplia d'aquests aspectes així com l'anàlisi estadística dels resultats, que avalua la magnitud de la millora en comparació amb els altres enfocaments.[EN] Machine Learning (ML) is known as the branch of artificial intelligence that gathers statistical, probabilistic, and optimization algorithms, which learn empirically. ML can exploit the knowledge and the experience that have been generated for years to automatically perform different processes. Therefore, ML has been applied to a wide range of research areas, from medicine to software engineering. In fact, in software engineering field, up to an 80% of a system's lifetime is spent on the maintenance and evolution of the system. The companies, that have been developing these software systems for a long time, have gathered a huge amount of knowledge and experience. Therefore, ML is an attractive solution to reduce their maintenance costs exploiting the gathered resources. Specifically, Traceability Link Recovery, Bug Localization, and Feature Location are amongst the most common and relevant tasks when maintaining software products. To tackle these tasks, researchers have proposed a number of approaches. However, most research focus on traditional methods, such as Latent Semantic Indexing, which does not exploit the gathered resources. Moreover, most research targets code, neglecting other software artifacts such as models. In this dissertation, we present an ML-based approach for fragment retrieval on models (FRAME). The goal of this approach is to retrieve the model fragment which better realizes a specific query in a model. This allows engineers to retrieve the model fragment, which must be traced, fixed, or located for software maintenance. Specifically, the FRAME approach combines evolutionary computation and ML techniques. In the FRAME approach, an evolutionary algorithm is guided by ML to effectively extract model fragments from a model. These model fragments are then assessed through ML techniques. To learn how to assess them, ML techniques takes advantage of the companies' knowledge (retrieved model fragments) and experience. Then, based on what was learned, ML techniques determine which model fragment better realizes a query. However, model fragments are not understandable for most ML techniques. Therefore, the proposed approach encodes the model fragments through an ontological evolutionary encoding. In short, the FRAME approach is designed to extract model fragments, encode them, and assess which one better realizes a specific query. The approach has been evaluated in our industrial partner (CAF, an international provider of railway solutions) and compared to the most common and recent approaches. The results show that the FRAME approach achieved the best results for most performance indicators, providing a mean precision value of 59.91%, a recall value of 78.95%, a combined F-measure of 62.50%, and a MCC (Matthews correlation coefficient) value of 0.64. Leveraging retrieved model fragments, the FRAME approach is less sensitive to tacit knowledge and vocabulary mismatch than the approaches based on semantic information. However, the approach is limited by the availability of the retrieved model fragments to perform the learning. These aspects are further discussed, after the statistical analysis of the results, which assesses the magnitude of the improvement in comparison to the other approaches.Marcén Terraza, AC. (2020). Design of a Machine Learning-based Approach for Fragment Retrieval on Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/158617TESI

Meat Slaughter and Processing Plants’ Traceability Levels Evidence From Iowa

In the United States (U.S.), there is no uniform traceability regulation across food sector. Food and Drug Administration (FDA) implemented one-step back and one-step forward traceability over the industries under its jurisdiction. U.S. Department of Agriculture (USDA), which oversees meat, poultry and egg production, requires some record keeping as part of food safety regulation. Particularly, a two-part-system has developed; live animal traceability and meat traceability with slaughter and processing plants in between. This paper studies the question of whether (and if so how) meat plants’ traceability levels vary with respect to the following factors; product specific (credence versus experience and search attributes, branded versus commodity meat, being exporter), organizational (spot market versus contracting), food safety related, and plant specific (a quality assurance system in place, number of sources, size, capital-labor ratio, etc.).traceability, food safety, quality assurances, animal ID, RFID,