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Various studies on the Learning Management System (LMS) have not examined the suitability of LMS features with the educational standards applicable in a country/region. This study aims to measure the suitability of LMS features with the National Higher Education Standards/Standar Nasional Pendidikan Tinggi (SN-Dikti) in Indonesia using the Feature-Oriented Domain Analysis (FODA) method. This research identifies explicitly LMS features in the assignment and assessment functions. Besides, this study recommends previous LMS features for future LMS development based on the assessment standards applicable in Indonesia. The results of the analysis in this study found the suitability of the three LMS and recommended LMS features for Lecturer and Student users.

This proposal is about a study we recently published in the IEEE Transaction of Software Engineering journal [4]. Context: Collaborative software engineering (CoSE) deals with methods, processes and tools for enhancing collaboration, communication, and co-ordination (3C) among team members [5]. CoSE can be employed to conceive different kinds of artifacts during the development and evolution of software systems. For instance, when focusing on software design, multiple stakeholders with different expertise and responsibility collaborate on the system design. Model-Driven Software Engineering (MDSE) provides suitable techniques and tools for specifying, manipulating, and analyzing modeling artifacts including metamodels, models, and transformations [1]. Collaborative MDSE consists of methods or techniques in which multiple stakeholders manage, collaborate, and are aware of each others' work on a set of shared models. A collaborative MDSE approach is composed of three main complementary dimensions: (i) a model management infrastructure for managing the life cycle of the models, (ii) a set of collaboration means for allowing involved stakeholders to work on the modelling artifacts collaboratively, and (iii) a set of communication means for allowing involved stakeholders to exchange, share, and communicate information within the team. Collaborative MDSE is attracting several research efforts from different research areas (e.g., model-driven engineering, global software engineering, etc.), resulting in a variegated scientific body of knowledge on the topic. Objective: In this study we aim at identifying, classifying, and understanding existing collaborative MDSE approaches. More specifically, our goal is to assess (i) the key characteristics of collaborative MDSE approaches (e.g., model editing environments, model versioning mechanisms, model repositories, support for communication and decision making), (ii) their faced challenges and limitations, and (iii) the interest of researchers in collaborative MDSE approaches over time and their focus on the three dimensions of collaborative MDSE. Method: In order to achieve this, we designed and conducted a systematic mapping study [6] on collaborative MDSE. Starting from over 3,000 potentially relevant studies, we applied a rigorous selection procedure [3] resulting in 106 selected papers, further clustered into 48 primary studies, along a time span of nineteen years. A suitable classification framework has been empirically defined and rigorously applied for extracting key information from each selected study. We collated, summarized, and analyzed extracted data by applying scientifically sound data synthesis techniques. Results: In addition to a number of specific insights, our analysis revealed the following key findings: (i) there is a growing scientific interest on collaborative MDSE in the last years; (ii) multi-view modeling, validation support, reuse, and branching are more rarely covered with respect to other aspects about collaborative MDSE; (iii) different primary studies focus differently on individual dimensions of collaborative MDSE (i.e., model management, collaboration, and communication); (iv) most approaches are language-specific, with a prominence of UML-based approaches; (v) few approaches support the interplay between synchronous and asynchronous collaboration. Conclusion: This study gives a solid foundation for a thorough identification and comparison of existing and future approaches for collaborative MDSE [2]. Those results can be used by both researchers and practitioners for identifying existing research/technical gaps to attack, better scoping their own contributions to the field, or better understanding or refining existing ones.

The application of search-based software engineering techniques to new problems is increasing. Feature location is one of the most important and common activities performed by developers during software maintenance and evolution. Features must be located across families of products and the software artifacts that realize each feature must be identified. However, when dealing with industrial software artifacts, the search space can be huge. We propose and compare five search algorithms to locate features over families of product models guided by latent semantic analysis (LSA), a technique that measures similarities between textual queries. The algorithms are applied to two case studies from our industrial partners (leading manufacturers of home appliances and rolling stock) and are compared in terms of precision and recall. Statistical analysis of the results is performed to provide evidence of the significance of the results. The combination of an evolutionary algorithm with LSA can be used to locate features in families of models from industrial scenarios such as the ones from our industrial partners.

Coordinating software reuse activities is a complex problem when considering collaborative software development. This is mainly motivated due to the difficulty in specifying how the artifacts and the knowledge produced in previous projects can be applied in future ones. In addition, modern software systems are developed in group working in separate geographical locations. Therefore, techniques to enrich collaboration on software development are important to improve quality and reduce costs. Unfortunately, the current literature fails to address this problem by overlooking existing reuse techniques. There are many reuse approaches proposed in academia and industry, including Framework Instantiation, Software Product Line, Transformation Chains, and Staged Configuration. But, the current approaches do not support the representation and implementation of collaborative instantiations that involve individual and group roles, the simultaneous performance of multiple activities, restrictions related to concurrency and synchronization of activities, and allocation of activities to reuse actors as a coordination mechanism. These limitations are the main reasons why the Reuse Description Language (RDL) is unable to promote collaborative reuse, i.e., those related to reuse activities in collaborative software development. To overcome these shortcomings, this work, therefore, proposes CollabRDL, a language to coordinate collaborative reuse by providing essential concepts and constructs for allowing group-based reuse activities. For this purpose, we extend RDL by introducing three new commands, including role, parallel, and doparallel. To evaluate CollabRDL we have conducted a case study in which developer groups performed reuse activities collaboratively to instantiate a mainstream Java framework. The results indicated that CollabRDL was able to represent critical workflow patterns, including parallel split pattern, synchronization pattern, multiple-choice pattern, role-based distribution pattern, and multiple instances with decision at runtime. Overall, we believe that the provision of a new language that supports group-based activities in framework instantiation can help enable software organizations to document their coordinated efforts and achieve the benefits of software mass customization with significantly less development time and effort.

The traditional teaching-learning processes used by Chilean universities are changing. This document describes two innovative strategies applied to teach software engineering (SE) at two universities in Chile. Teaching experiences are part of our ongoing effort to improve the current teaching-learning strategies of SE in the mentioned universities. In this paper, we describe our teaching experiences using both problem-based learning (PBL) and an agile Software engineering methodology, to achieve learning outcomes and to improve students’ participation during classes. We present two ways to correlate activities of a software project, these activities are tasks of a software methodology, and an active learning strategy based on PBL. The strategies used to verify the accomplishment of these goals are also detailed.

Software reuse is a development strategy in which existing software components, called reusable assets, are used in the development of new software systems. There are many advantages of reuse in software development, such as minimization of development efforts and improvement of software quality. New methods for reusable asset extraction are essential to achieve these advantages. Extraction methods may be used in different contexts including software product lines derivation. However, few methods have been proposed in literature for reusable asset extraction and recommendation of these reuse opportunities. In this paper, we propose a method for extraction of reuse opportunities based on naming similarity of two types of object-oriented entities: classes and methods. Our method, called JReuse, computes a similarity function to identify similarly named classes and methods from a set of software systems from a domain. These classes and methods compose a repository with reuse opportunities. We also present a prototype tool to support the extraction by applying our method. We evaluate the method with 38 e-commerce information systems mined from GitHub. As a result, we observe that our method is able to identify classes and methods that are relevant in the e-commerce domain.

One important issue in constructing a pattern recognition system is feature selection. The goal of feature selection, including feature ranking and feature subset selection, is to identify target-relevant features. When applied to high-dimensional and small-sized (HDSS) data, e.g. microarray gene expression data, the commonly used feature selection algorithms encounter problems such as over-sensitivity to variations in training data, i.e. robustness issue. The aim of this thesis is to address the robustness issue of feature selection for the HDSS data. Firstly, a novel criteria normalization algorithm has been proposed for multi-criterion combination to improve robustness of feature ranking. Traditional feature ranking criteria are apt to produce inconsistent ranking results even with slight perturbations in training samples when applied to HDSS data. A widely used strategy for solving the inconsistency problems is multi-criterion combination. But one crucial problem in multi-criterion combination is how to normalize feature scores from different criteria. In the thesis, a new feature importance transformation algorithm based on resampling and permutation is proposed for score normalization. Experimental studies on four popular gene expression data sets show that the multi-criterion combination based on the proposed score normalization produces gene rankings with improved robustness. Secondly, a multi-criterion fusion-based recursive feature elimination (MCF-RFE) algorithm has been developed with the goal of improving both classification performance and robustness of feature subset selection. Feature subset selection often aims to select a compact subset of features to build a pattern classifier with reduced complexity. From the perspective of pattern analysis, producing stable or robust solution is also a desired property of a feature subset selection algorithm. In the thesis, we analyze the robustness issue existing in feature subset selection for HDSS gene expression data, and propose the MCF-RFE algorithm. Experimental studies on five gene expression data sets show that the MCF-RFE algorithm outperforms the commonly used benchmark feature selection algorithm SVM-RFE. Thirdly, a new regularized linear discriminant analysis (LDA) based algorithm has been proposed for robust feature selection of HDSS data. When applied to gene expression data which usually have high dimensionality, small sample size and class imbalance (i.e. great discrepancy in the number of samples between classes), LDA-based feature selection encounters problems such as singularity of scatter matrix, overfitting, overwhelming and prohibitive computational complexity. In the thesis, we propose a new regularization technique giving more emphasis to minority class, with the expectation of improving overall performance by alleviating overwhelming of majority class to minority class as well as overfitting in minority class. In addition, an incremental implementation of LDA-based feature selection has been developed to reduce computational overhead. Comparative studies on five gene microarray problems show that LDA with the new regularization can produce gene subsets with excellent performance in both classification and robustness. Fourthly, a framework of reducing feature redundancy has been proposed. In order to improve compactness of feature subsets, the usual practice is to utmostly remove redundancy. But this strategy does not necessarily help in some problems such as HDSS gene expression data analysis. We argue that a moderate degree of feature redundancy should be retained in feature subset for improving both classification and robustness. The proposed framework based on this argument is flexible and can be applied to any feature ranking algorithms. We have implemented the framework with the popular feature ranking algorithm{Fisher's ratio, and conducted experiments on five gene expression data sets. The experimental studies show that the proposed framework produces feature subsets with improved classification performance and good performance in robustness. DOCTOR OF PHILOSOPHY (EEE)

Every discipline has its own specific knowledge that has been accumulated and refined over time. In the aerospace industry, for example, the domain knowledge of multidisciplinary optimization has grown and matured. The same has happened with domain knowledge related to modularity in the consumer product design industry. Knowledge from these domains has carried over to other domains such as automotive, medical, and defense, and has enabled advances in these disciplines. One domain that has been underserved by the advanced engineering methodologies coming from other disciplines is the domain of design for the developing world. Exploring the use of engineering domain knowledge to alleviate poverty is a valuable study that will open opportunities to use engineering to benefit resource poor individuals. This paper explores the domain knowledge of modularity and multi-objective optimization and applies it to the domain of design for the developing world by introducing the concept of collaborative products to assist the resource poor individuals. Can knowledge from one domain be used in a new domain, and if so, what would it look like? In this paper, a general methodology is presented, followed by a simple example to illustrate the design of a collaborative product for the developing world. The paper suggests that by using domain knowledge from a non-related domain paired with the method presented, products can be designed and optimized for collaborative performance with potential to both generate new income and save money for the end customers.

Software configuration, which aims to customize the software for different users (e.g., Linux kernel configuration), is an important and complicated task. In software product line engineering (SPLE), feature oriented domain analysis is adopted and feature model is used to guide the configuration of new product variants. In SPLE, product configuration is an optimal feature selection problem, which needs to find a set of features that have no conflicts and meanwhile achieve multiple design objectives (e.g., minimizing cost and maximizing the number of features). In previous studies, several multi-objective evolutionary algorithms (MOEAs) were used for the optimal feature selection problem and indicator-based evolutionary algorithm (IBEA) was proven to be the best MOEA for this problem. However, IBEA still suffers from the issues of correctness and diversity of found solutions. In this paper, we propose a dual-population evolutionary algorithm, named IBED, to achieve both correctness and diversity of solutions. In IBED, two populations are individually evolved with two different types of evolutionary operators, i.e., IBEA operators and differential evolution (DE) operators. Furthermore, we propose two enhancement techniques for existing MOEAs, namely the feedback-directed mechanism to fast find the correct solutions (e.g., solutions that satisfy the feature model constraints) and the preprocessing method to reduce the search space. Our empirical results have shown that IBED with the enhancement techniques can outperform several state-of-the-art MOEAs on most case studies in terms of correctness and diversity of found solutions.

In this work we propose a development methodology aiming to bridge the gap between approaches used by (e)-science communities to develop their modeling frameworks, and model driven engineering approaches used to develop modeling frameworks with similar complexity. The proposed methodology relies on a sound integration of UML-MARTE, CSP-OZ, and PyCSP. We show, in this paper, how to exploit this similarity to bridge the gap between both approaches. A first contribution of this paper consists in proposing a sound software development methodology for the construction of critical reactive systems in general and multi-scale simulation frameworks in particular. A second contribution consists in adding a new sub-profile for MARTE, the so-called SSRM (Specific Software Resource Model), dedicated to the modeling of multi-scale simulation frameworks. MUSCLE [9], a recent distributed multi-scale simulation framework, serves as a case study in this work. The proposed SSRM sub-profile is intended to define specific software resources that capture a subset of MUSCLE core concepts. A third contribution consists in sketching a formal semantic framework for our software development methodology aiming at ensuring a sound integration (from a semantic point of view) of UML-MARTE, CSP-OZ and PyCSP.

Ontological context model that can be instantiated for the software domain.Context that supports information sharing and reuse among tools within the software domain.Application examples for the use of the context model in the software domain.Evaluation of the model to illustrate the applicability for mentor assignment. Recent years have witnessed rapid advances in the use of contextual information in ubiquitous and ambient computing. Such information improves situated cognition and awareness as well as stakeholders' usage experience. While domains such as Web 3.0 - the next generation of the web - have made context-awareness a main requirement of their solution space, the software engineering domain still lacks the same rate of adoption. In our research, we introduce an ontology based context-aware meta-model that takes advantage of Semantic Web technologies to capture and formalize context information. Providing such formal context representation allows us to make context information an integrated and reusable part of the software engineering domain. We present several case studies related to the software evolution domain to illustrate the benefit of sharing and reusing context for various software engineering tasks, such as mentor recommendation, code search, and result ranking.

Establishing attribute model to illustrate attributes to be looked into.Establishing evaluation model to illustrate computation logic of evaluation.Establishing classifies model to illustrate quality levels and mapping conditions.Analyzing quality bottleneck problems based on evaluation model data. Context: Domain software plays an important role in the management of many enterprises; therefore, its quality is critical to these enterprises and impacts its selection, maintenance, and service quality evaluation to a great extent. Consequently, how to evaluate the quality of domain software has become an area that deserves more research. The existing research pays much attention to software for specific purposes and fields, or to specific stage of the software, which is not enough.Objective: This paper proposes a systematic quality evaluation method, which fully considers the generality, domain features, and application environment of domain software, and also supports subsequent analysis.Method: The quality evaluation method proposed in this paper defines all aspects of the domain software to be inspected by establishing an attribute model, obtains basic data on evaluation by analyzing and measuring evidence, tailor-makes the computing logic of quality evaluation score by establishing an evaluation model, and classifies domain software quality by establishing level model. This research verifies the applicability of evaluation by applying this method to management information system (MIS) in the field of a power plant.Results: The case example not only indicates that the proposed method can be applied to the quality evaluation and analysis of the power plant's MIS, but also its ease of usability.Conclusion: The method is applicable and can be easily used for the quality evaluation and analysis of the MIS in a power plant. Based on mechanisms, such as model customization and weight determination, this method also supports the quality evaluation and analysis of software applied in different fields. In the future, there will be a need to improve the objectivity and automation degree of the evaluation, and to apply and verify the method according to the different types of software in various fields.

Defining characteristics of a problem domain continues to challenge developers of visualization software although it is essential for designing both tools and resulting visualizations. Additionally, effectiveness of a visualization software tool often depends on the context of systems and actors within the domain problem. The nested blocks and guidelines model is a useful template for informing design and evaluation criteria for visualization software development because it aligns design to need. Characterizing the outermost block of the nested model—the domain problem—is challenging, mainly due to the nature of contemporary domain problems, which are dynamic and by definition difficult to problematize. We offer here our emerging conceptual model, based on the central question in our research study—what visualization works for whom and in which situation—to characterize the outermost block, the domain problem, of the nested model. We apply examples from a 3-year case study of visualization software design and development to demonstrate how the conceptual model might be used to create evaluation criteria affecting design and development of a visualization tool.

Along with the boom of Mobile services and the thriving Model Driven Architecture (MDA), we must consider the growing significance and utility of modeling in the development of software and solutions. The main advantages of MDA are the ability to transform one PIM into several PSMs, one for each platform or technology in which the final system will be deployed, and the automatic code generation that implements the system for those platforms from the corresponding PSMs. Service-oriented architectures (SOA) are also touted as the key to business agility, especially when combined with a model-driven approach. Model-Driven Architecture (MDA) is a well-developed concept that fits well with SOA, but until now it has been a specialized technique that is beyond practical application scope of most enterprises. Model driven and Domain engineering is the foundation for emerging “product line” software development approaches and affects the maintainability, understandability, usability, and reusability characteristics of family of similar systems. However, the existing domain engineering methods do not elicit information necessary for the Component-based Software Development process, such as, selecting and configuring appropriate components. In this paper, we suggest a method that systematically defines, analyzes and designs a crop monitoring frameworks domain to enhance reusability effectively in Model Driven Engineering and Component-based Software Development. For this, we extract information objectively that can be reused in a crop monitoring domain from the requirement analysis phase. We sustain and refine crop monitoring information, and match them to artifacts of each phase in domain engineering. Through this method, reusable domain components and malleable domain architecture can be produced. In addition, we show the practical applicability and features of our approach via a new clouding and IOT technology.

The impact of Covid-19 on the world of education requires the implementation of online learning. Information and computer technology can provide solutions in education through the Learning Management System (LMS). Canvas LMS is a form of LMS that can be utilized by lecturers and students in online learning during the Covid-19 pandemic. In this study, researchers analyzed the features of Canvas LMS using the Feature-Oriented Domain Analysis (FODA) method. Researchers also conducted a mapping of canvas LMS features with the National Standard of Higher Education (SN Dikti). This study showed two stages of validation by Education and LMS experts to verify the results of the analysis. At the end of the course, we recommended a new LMS feature that complies with SN Dikti for Lecturer and Student users.

Enhancing software maintenance productivity is very important in the modern information society, which demands for software surges. In this paper propose Feature Oriented Domain Analysis (FODA) tool support is necessary to enhance productivity of software. Especially, a refactoring tool that can alter the internal system in such a way that, it does not alter the external behavior of the code which improves the internal structure. New developed FODA tool for refactoring the text editor that is Notepad++. FODA tool is used to support software refactoring for improving quality of the software. We can reuse after refactoring the software. The developers can easily access and handle source code. The implementation method of software refactoring using FODA tool is discussed in this paper and also realizes the refactoring in simple application software that is text editor in Notepad++. Refactoring is also applicable to Automobiles spare parts.

Smart System has implemented in government sector. There are varies Implementation that was utilized by research activities for numerous domains is very broad. Besides that, the Industry, transportation and health, also where such a system is incredibly beneficial. This study discuss supply chain and governmental link issue, coordination of all stakeholder in supply chain has to reflect the government role. It support with the condition in Indonesian government environment is unique. It is a challenge to construct smart system based on Feature Oriented Domain Analysis (FODA) approach. It can produce software product line (SPL). We proposed framework for develop software product line for smart supply chain in government sector. It is used to enhance and improve the development of software systems by multiple software system developers. It will be a guidance for construct smart government, and more specificity in supply chain for government system area environment. It is called SGSC Framework. It consists of four layers, such as optimization layer, integration layer, supply chain layer and data layer.

In this paper, several problems functioning as the obstacles against software reuse were summarized. Among them, the issues dealt with in this paper include the effective method for constructing the library, the proper structure of the library, and the efficient retrieval technique. The knowledge-based approach and the information retrieval approach were integrated to construct and manage the library. The former is on the objectoriented model. Basically the object-oriented library is based on the classes and organized by inheritance. Because inheritance hierarchy is based on syntactical information, it dose not present the relationship of functionality. Using the information retrieval approach, the index file which characterizes the component and similarity among the components can be analyzed. Especially, we focused on the reusable library for the object-oriented programming environments.

Model-driven IS engineering methods invoke the IS application domain modelling met- hods to acquire essential characteristics of organizational systems (enterprises). Business modelling for value creation is a relatively separate area, meanwhile it correlates with the IS application domain modelling methodologies and gives new insights for enhancement of enterprise modelling, business process modelling and BP management modelling approaches. The IS application domain and the business domain modelling are not isolated and could be investigated using the same paradigm of modelling. Yet there is some uncertainty in model-driven approaches towards the understanding of the enterprise management activities. A problematic consistency of modelling approaches indicate a need for a systemic analysis of IS application domain modelling concepts. The internal modelling paradigm is used for analysis of an enterprise management activity as a self-managed system, and hereby the lack of the conceptual basis for domain modelling in IS engineering is determined. This approach is aimed to reveal hidden information transactions of the business management activities. The understanding of the IS application domain as a self-managed system allowed to redefine such concepts as management transaction, management function and enterprise process. The metastruc- ture of management transaction is defined and illustrated for business management layer and IS development layer.

The objective of feature modeling is to foster software reuse by enabling to explicitly and abstractly express commonality and variability in the domain. Feature modeling is used to configure other models and, eventually, code. These software assets are being configured by the feature model based on the selection of variable features. However, selecting a feature is far from a naive component based approach where feature inclusion would simply mean including the corresponding component. More often than not, feature inclusion affects several places in models or code to be configured requiring their nontrivial adaptation. Thus, feature inclusion recalls transformation and this is at heart of the approach to feature model driven generation of software artifacts proposed in this paper. Features are viewed as transformations that may be executed during the generative process conducted by the feature model configuration. The generative process is distributed in respective transformations enabling the developers to have a better control over it. This approach can be applied to modularize changes in product customization and to establish generative software product lines by gradual refactoring of existing products.

For many years, the progress in agent-oriented development has focused on tools and methods for particular development phases. This has not been enough for the industry to accept agent technology as we expected. Our hypothesis is that the Agent-Oriented Software Engineering (AOSE) community has not recognized the kind of development methods that industry actually demands. We propose to analyze this hypothesis starting with a more precise definition of what an AOSE methodology should be. This definition is the first step for a review of the current progress of an illustrative selection of methodologies, looking for missing elements and future lines of improvement. The result is an account of how well the AOSE community is meeting the software lifecycle needs. It can be advanced that AOSE methodologies are far from providing all the answers industry requires and that effort has grounded mainly in requirements, design, and implementation phases.

Acquiring relevant business concepts is a crucial first step for any software project for which the software experts are not domain experts. The wealth of information buried within an organization's written documentation is a precious source of concepts, relationships and attributes which can be used to model the enterprise's domain. The lack of targeted extraction tools can make perusing through this type of resource a lengthy and costly process. We propose a domain model focused extraction process aimed at the rapid discovery of knowledge relevant to the software expert. To avoid undesirable noise from high-level linguistic tools, the process is mainly composed of positive and negative base filters that are less error prone and more robust. The extracted candidates are then reordered using a weight propagation algorithm based on structural hints from source documents. When tested on French text corpora from public organizations, our process performs 2.7 times better than a statistical baseline for relevant concept discovery. A new metric to assess the performance discovery speed of relevant concepts is introduced. The annotation of a gold standard definition of software engineering oriented concepts for knowledge extraction tasks is also presented.

A unified feature modeling framework is suggested based on semantic exploration.The associative feature technology is extended into chemical process domain.A set of chemical process features is proposed to represent process flexibly.Functional feature is suggested to create an association mechanism across domains.The proposed framework offers systematic management of engineering associations. Currently, the lack of interoperability across different engineering disciplinary domains remains a big issue in industrial practice, such as those major chemical process projects. Detailed engineering information is mainly exchanged verbally or, to a very limited extent, manually by technical documents, and hence the interdisciplinary dependencies, especially those tedious and error-prone references across domains, are not well maintained. The interoperability is hindered by the heterogeneous domain models used, and engineering semantics are not well maintained during the project lifecycle. This paper attempts to solve the informatics interoperability issue by proposing an inter-domain functional feature framework based on the associative feature mechanism and the exploration of commonalities of product and process models between chemical and mechanical domains. The schemas of feature models are developed explicitly to represent the characteristics of multi-domain entities and flows, while a mapping mechanism is realized to support inter-domain information sharing. Inter-feature relations are systematically managed so that the intricate engineering knowledge is incorporated into design tasks. The prototype software developed shows that design conflicts can be detected by the system intelligently; and the effectiveness towards engineering consistency management is promising.

This article presents the state of the art and practice in system and software variability. It describes the trends driving the increasing importance of variability management, proposes a research agenda, and provides an overview of the articles in this special issue. The Web extra at http://youtu.be/80DAUGahNAY is an audio recording of Davide Falessi speaking with Guest Editors Rafael Capilla and Rich Hilliard about the state of the art and practice in system and software variability.

Recently proposed model repositories aim to support specific needs--for example, collaborative modeling, the ability to use different modeling tools in software life-cycle management, tool interoperability, increased model reuse, and the integration of heterogeneous models.

One of the reasons for the lack of rigor in software engineering compared to physical and behavioral sciences is that the theories that underpin our work, both as software engineers and as software engineering researchers, have not been given enough attention. To provide a step forward towards greater rigor, a framework has been created with which to explore theories of software engineering and software engineering research. This framework provides a simple theory modeling language and model calculus to explore informally described theories and to generate the results of composing modeled theories. To illustrate and evaluate this framework, a general theory about software engineering is presented and then two simple theories, D and E, are proposed as the basis for laying out a unified theoretical foundation for software engineering and software engineering research. Software Engineering consists of two logical parts: design, and empirical evaluation (both terms used in their broadest senses). Theory D is the theoretical basis for the design part, and theory E is the theoretical basis for empirical evaluation. These two theories are then composed in various ways to lay out a space (a taxonomy, or ontology if you will) for software engineering and software engineering research. Finally, it is claimed that software engineering and software engineering research (both fully integrated with empirical evaluations) are models (in the logical sense) for these atomic and composed theories. To further evaluate the framework, examples are provided of modeling (implicit) theories found in a number of software engineering (theory) papers. The results of this research are: 1) a scientific elegance in creating larger more complex theories out of simpler theories, 2) an elegant way of explaining the complexity of software engineering and software engineering research, and 3) a theory modeling language and model calculus for composing the resulting theoretical models.

This work presents the integration of different computational techniques to specify and design a new computational electromagnetic simulator, called Free Visual FDTD 2D (FV-FDTD-2D), to support the Telecommunications teaching-learning process. Some Software Engineering, human-computer interface (HCI) and an advanced numerical method, based on Finite Difference Time Domain (FDTD), were successfully integrated. It provides an efficient processing and usable software, which detailed documents for developers and comprehensive graphical output results. The FV-FDTD-2D has been adopted in some technical lectures related to telecommunications, presenting successful evaluations by the attempted public.

This paper proposes a methodology for ontological engineering with an aim to develop reusable ontologies. The proposed methodology combines experience of developing ontology engineering using a ‘good’ practice with established methodologies and concomitantly implementing reusability in the eSymbiosis ontology.

Corporate organizations sometimes offer similar software products in certain domains due to former company mergers or due to the complexity of the organization. The functional overlap of such products is an opportunity for future systematic reuse to reduce software development and maintenance costs. Therefore, we have tailored existing domain analysis methods to our organization to identify commonalities and variabilities among such products and to assess the potential for software product line (SPL) approaches. As an exploratory case study, we report on our experiences and lessons learned from conducting the domain analysis in four application cases with large-scale software products. We learned that the outcome of a domain analysis was often a smaller integration scenario instead of an SPL and that business case calculations were less relevant for the stakeholders and managers from the business units during this phase. We also learned that architecture reconstruction using a simple block diagram notation aids domain analysis and that large parts of our approach were reusable across application cases.

Model-driven architecture (MDA) is a contemporary strategy for software development using three distinct kinds of models: (1) models expressing the problem to be solved in the semantics of its domain; (2) models of the solution space in terms of hardware and software elements; and (3) models that map from the domain space to the solution space. We explore an adaptation of the MDA strategy to meet some key application challenges in creating simulation models to support system design and operating decisions. For a given simulation tool, our approach requires a single transformation model that is applicable for any domain for which the simulation tool is appropriate. For a given application domain, our approach allows an iterative development of the domain semantics. Our approach empowers the domain stakeholders with engineering tools for defining the problem to be solved and formally integrating that definition with the simulation solver. Finally, our approach captures knowledge about how to translate from the problem domain to the simulation tool so that knowledge can be taught, reused, and refined.

Product line engineering and model based engineering are two powerful engineering approaches that each bring significant advantages to system engineering projects. This paper explores how three companies - Raytheon, General Dynamics, and General Motors - are combining these two paradigms in unique and innovative ways in very challenging application domains to achieve engineering goals of critical importance to them.

This demonstration shows how domain-specific languages are applied with MetaEdit+ tool in various kinds of product lines, ranging from industry automation to consumer electronics. In the demonstration practical examples are illustrated and executed covering both domain engineering and application engineering. In particular evolution and versioning of domain knowledge and application knowledge is detailed and demonstrated.

Feature trees have been the standard data structure for representing product diversity in feature-based systems and software product line engineering (PLE). For basic product lines of modest size or complexity, one or several modular feature trees can be sufficient for managing the and resolving the variation present across the engineering assets in the systems engineering 'V' --- from requirements, to design, through implementation, verification, validation, documentation, and more --- in the software, mechanical, and electrical disciplines. However, enterprises seeking to adopt PLE at all levels of their organization, including areas such as product marketing, portfolio planning, manufacturing, supply chain, product sales, product service and maintenance, Internet-of-Things, resource planning, and much more are finding that thousands of nonengineering users need different views and interaction scenarios with a feature diversity representation. This paper describes a feature ontology (a specification of the meaning of terms in the feature modeling realm) that is suitable for managing the feature-based product line engineering and operations in the largest and most complex product line organizations. This ontology is based on layers of abstraction that each incrementally constrain the complexity and combinatorics and targets specific roles in the organization for greater degrees of efficiency, precision, and automation across an entire business enterprise.

Formal methods are a usual means to avoid errors or bugs in the development, adjustment and maintenance of both software and knowledge bases. This chapter provides a formal method to refine a knowledge base based on insides about its correctness derived from its use in practice. The objective of this refinement technique is to overcome particular invalidities revealed by the application of a case-oriented validation technology, i.e. it is some kind of “learning by examples”. Approaches from AI or Data Mining to solve such problems are often not useful for a system refinement that aims at is an appropriate modeling of the domain knowledge in way humans would express that, too. Moreover, they often lead to a knowledge base which is difficult to interpret, because it is too far from a natural way to express domain knowledge. The refinement process presented here is characterized by (1) using human expertise that also is a product of the validation technique and (2) keeping as much as possible of the original humanmade knowledge base. At least the second principle is pretty much adopted from Software Engineering. This chapter provides a brief introduction to AI rule base refinement approaches so far as well as an introduction to a validation and refinement framework for rulebased systems. It also states some basic principles for system refinement, which are adopted from Software Engineering. The next section introduces a refinement approach based on these principles. Moreover, it considers this approach from the perspective of the principles. Finally, some more general conclusions for the development, employment, and refinement of complex systems are drawn. The developed technology covers five steps: (1) test case generation, (2) test case experimentation, (3) evaluation, (4) validity assessment, and (5) system refinement. These steps can be performed iteratively, where the process can be conducted again after the improvements have been made.

Finding faults in early stages of software development decreases the cost of testing and increases system reliability. For safety-critical software, it is vital to perform throughly verification procedures. Model-Checking can find many faults in an early stages, and therefore, it represent an important verification approach. In this paper, we use domain engineering to capture important features of model-checking tools. We used FODA method (Feature Oriented Domain Analysis) to describe the common features of important model-checking tools. We expect our results to help software engineers to identify tools that better fit their needs. Our results can also help in improving these tools.

Software reuse in the early stages is a key issue in rapid development of applications. This article introduces a metaprocess-oriented methodology based on the model reuse as software assets, and starting from the domain specification and analysis phases. The approach includes the definition of a conceptual level to adequately represent the domain and a reuse process to specify the metaprocess as software assets. The methodology has been applied successfully in the field of e-health, but our work also describes advances in reuse of models for implementation in other contexts, contributing to improved productivity in software development.

Domain-specific modelling is used to construct and realise the different application models upon the same specific domain for software reuse. The paper integrates domain-specific modelling and web service techniques with model-driven development, and proposes a unified approach named SODSMI (Service Oriented executable Domain-Specific Modelling and Implementation) to build the executable domain-specific model so as to achieve the target of model-driven development and reuse. The approach is organised by domain space, which is employed as the elementary unit of the domain-specific modelling and implementation framework. It makes software reuse at the domain level, realises the reuse of domain knowledge, and openly extends the range and scale of domain-specific model and its implementation.

Software Product Line Engineering (SPLE) has been proven to reduce development and maintenance costs, improving the time-to-market, and increasing the quality of product variants developed from a product family via systematic reuse of its core assets. SPLE has been successfully used in the development of safety-critical systems, especially in automotive and aerospace domains. Safety-critical systems have to be developed according to safety standards, which demands safety analysis, Fault Tree Analysis (FTA), and assurance cases safety engineering artefacts. However, performing safety analysis, FTA, and assurance case construction activities from scratch and manually for each product variant is time-consuming and error-prone, whereas variability in safety engineering artefacts can be automatically managed with the support of variant management techniques. As safety is context-dependent, context and design variation directly impact in the safety properties changing hazards, their causes, the risks posed by these hazards to system safety, risk mitigation measures, and FTA results. Therefore, managing variability in safety artefacts from different levels of abstraction increases the complexity of the variability model, even with the support of variant management techniques. To achieve an effective balance between benefits and complexity in adopting an SPLE approach for safety-critical systems it is necessary to distinguish between reusable safety artefacts, whose variability should be managed, and those that should be generated from the reused safety artefacts. On the other hand, both industry and safety standards have recognized the use of model-based techniques to support safety analysis and assurance cases. Compositional safety analysis, design optimization, and model-based assurance cases are examples of techniques that have been used to support the generation of safety artefacts required to achieve safety certification. This thesis aims to propose a model-based approach that integrates model-based development, compositional safety analysis, and variant management techniques to support the systematic reuse and generation of safety artefacts in safety-critical software product line engineering. The approach contributes to reduce the effort and costs of performing safety analysis and assessment for a particular product variant, since such analysis is performed from the reused safety artefacts. Thus, variant-specific fault trees, Failure Modes and Effects Analysis (FMEA), and assurance case artefacts required to achieve safety certification can be automatically generated with the support the model-based safety analysis and assurance case construction techniques. Engenharia de Linha de Produtos de Software (ELPS) contribui para a redução dos custos de desenvolvimento e de manutenção, a melhoria do time-to-market, e o aumento da qualidade de produtos desenvolvidos a partir de uma família de produtos por meio do reuso sistemático dos ativos principais da linha de produtos. A ELPS vem sendo utilizada com sucesso no desenvolvimento de sistemas embarcados críticos, especificamente nos domínios de sistemas automotivos e aeroespaciais. Sistemas embarcados críticos devem ser desenvolvidos de acordo com os requisitos definidos em padrões de segurança, que demandam a produção de artefatos de análise de segurança, árvores de falhas e casos de segurança. Entretanto, a realização de atividades de análise de segurança, análise de árvores de falhas e construção de casos de segurança de forma manual para cada produto de uma linha de produtos é uma tarefa demorada e propensa a erros. O gerenciamento de variabilidade em artefatos de análise de segurança pode ser automatizado com o apoio de técnicas de gerenciamento de variabilidades. Em virtude de safety ser uma propriedade dependente de contexto, a variabilidade no projeto e contexto inerente uma linha de produtos software impacta na definição de propriedades de segurança do sistema, modificando as ameaças à segurança do sistema, suas causas e riscos, medidas de mitigação aplicáveis, e resultados de análise de árvore de falhas. Dessa forma, gerenciar variabilidades em artefatos relacionados à safety em diferentes níveis de abstração aumenta a complexidade do modelo de variabilidade mesmo com o apoio de técnicas de gerenciamento de variabilidades. Para alcançar o equilíbrio eficaz entre os benefícios e a complexidade da adoção de uma abordagem de ELPS para o desenvolvimento de sistemas embarcados críticos é necessário fazer a distinção entre artefatos de safety reusáveis, em que a variabilidade deve ser gerenciada, e artefatos de safety que devem ser gerados a partir de artefatos reusáveis. Por outro lado, tanto a indústria quanto os padrões de segurança têm reconhecido o uso de técnicas dirigidas a modelos para apoiar a análise segurança e a construção de casos de segurança. Técnicas de análise de segurança composicional e otimização de projeto, e de construção de casos de segurança dirigido a modelos vêm sendo utilizadas para apoiar a geração de artefatos de safety requeridos para certificação. O objetivo desta tese é a proposta de uma abordagem dirigida a modelos que integra técnicas de desenvolvimento dirigido a modelos, análise de segurança composicional e otimização de projeto, e construção de casos de segurança dirigido a modelos para apoiar o reuso sistemático e a geração de artefatos de safety em engenharia de linhas de produtos de sistemas embarcados críticos. A abordagem proposta reduz o esforço e os custos de análise e avaliação de segurança para produtos de uma linha de produtos, uma vez que tal análise é realizada a partir de artefatos de safety reusados. Assim, artefatos como análises de árvores de falhas e de modos de falha e efeitos, e casos de segurança requeridos para certificação podem ser gerados automaticamente com o apoio de técnicas dirigidas a modelos.

Building a software product line (SPL) is a systematic strategy for reusing software within a family of related systems from some application domain. To define an SPL, domain analysts must identify the common and variable aspects of systems in the family and capture this information so that it can be used effectively to construct specific products. Often analysts record this information using a feature model expressed visually as a feature diagram. The overall goal of this project is to enable wider use of SPLs by identifying relevant concepts, defining systematic methods, and developing practical tools that leverage familiar web programming technologies. This paper presents a novel approach to specification of feature models: capture the details using automatically generated user interfaces, encode the models in a relational database, and then validate the models and construct specific products using SQL.

Nowadays, reusing software applications encourages researchers and industrials to collaborate in order to increase software quality and to reduce software development costs. However, effective reuse is not easy and only a limited portion of reusable models actually offers effective evidence regarding their appropriateness, usability and/or effectiveness. Focusing reuse on a particular domain, such as marine ecology, allows us to narrow the scope; and along with a systematic approach such as software product line development, helps us to potentially improving reuse. From our experiences developing a subdomain-oriented software product line (SPL for the marine ecology subdomain), in this paper we describe semantic resources created for assisting this development and thus promoting systematic software reuse. The main contributions of our work are focused on the definition of a standard conceptual model for marine ecology applications together with a set of services and guides which assist the process of product derivation. The services are structured in a service taxonomy (as a specialization of the ISO 19119 std) in which we create a new set of categories and services built over a conceptual model for marine ecology applications. We also define and exemplify a set of guides for composing the services of the taxonomy in order to fulfill different functionalities of particular systems in the subdomain. Fil: Buccella, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Comahue. Facultad de Informatica; Argentina Fil: Cechich, Susana Alejandra. Universidad Nacional del Comahue. Facultad de Informatica; Argentina Fil: Pol'la, Matias Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Comahue. Facultad de Informatica; Argentina Fil: Arias, Maximiliano Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Comahue. Facultad de Informatica; Argentina Fil: Doldan, María del Socorro. Universidad Nacional del Comahue. Centro de Investigacion Aplicada y Transferencia Tecnologica En Recursos Marinos "almirante Storni". - Provincia de Rio Negro. Ministerio de Agricultura, Ganaderia y Pesca. Centro de Investigacion Aplicada y Transferencia Tecnologica En Recursos Marinos "almirante Storni". - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Centro Nacional Patagonico. Centro de Investigacion Aplicada y Transferencia Tecnologica En Recursos Marinos "almirante Storni". ; Argentina; Argentina Fil: Morsan, Enrique Mario. Universidad Nacional del Comahue. Centro de Investigacion Aplicada y Transferencia Tecnologica En Recursos Marinos "almirante Storni". - Provincia de Rio Negro. Ministerio de Agricultura, Ganaderia y Pesca. Centro de Investigacion Aplicada y Transferencia Tecnologica En Recursos Marinos "almirante Storni". - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Centro Nacional Patagonico. Centro de Investigacion Aplicada y Transferencia Tecnologica En Recursos Marinos "almirante Storni". ; Argentina; Argentina

Domain knowledge for various decision-making activities of Software Engineering (SE) is rarely available in a structured or well-formalised form. Owing to lack of the well-informed knowledge, decision making for different kinds of predictions and estimations in SE domain is a challenge. Maintenance and elicitation of domain knowledge is an overwhelming task and causes the knowledge acquisition bottleneck. Most of the artificial intelligence techniques of prediction and estimation do not work in absence of complete and structured knowledge. Case-based reasoning (CBR) is a lazy learning paradigm of artificial intelligence which takes care of this challenge and helps to reduce the knowledge availability bottleneck. This technique exploits the similar experience of past which may be available in unstructured form, and improves its learning curve with passage of time. In literature, CBR has been successfully applied in various areas of SE, but there is lack of single systematic panoramic picture which might have highlighted the potential research questions in this direction. In this study, the author has presented a comprehensive and panoramic systematic mapping study of various CBR applications in SE domain, and identified some promising future research directions.