A Conceptual UX-aware Model of Requirements

User eXperience (UX) is becoming increasingly important for success of software products. Yet, many companies still face various challenges in their work with UX. Part of these challenges relate to inadequate knowledge and awareness of UX and that current UX models are commonly not practical nor well integrated into existing Software Engineering (SE) models and concepts. Therefore, we present a conceptual UX-aware model of requirements for software development practitioners. This layered model shows the interrelation between UX and functional and quality requirements. The model is developed based on current models of UX and software quality characteristics. Through the model we highlight the main differences between various requirement types in particular essentially subjective and accidentally subjective quality requirements. We also present the result of an initial validation of the model through interviews with 12 practitioners and researchers. Our results show that the model can raise practitioners' knowledge and awareness of UX in particular in relation to requirement and testing activities. It can also facilitate UX-related communication among stakeholders with different backgrounds.Comment: 6th International Working Conference on Human-Centred Software Engineerin

Conceptual Model for Developing Creativity in Batik Industry

The purpose of this research is to develop a conceptual model of creativity in batik industry. This model was developed by conducting a study from previous research that discuss important factors for the development of creativity. This conceptual model was built based on four variable, namely creative person, intrinsic motivation, job skills training, and creative organizational climate. Creative person will stimulate the creativity development in batik industry. A creative person are more able to improve their creativity if they have intrinsic motivation, given some training that related with the job skills they needed, and supported by organization that have positive climate (climate in organization that respects creativity, provide opportunities, time, facilities, infrastructure and incentives to employees to think about, designing, researching and developing new products that better and more innovative). For the further research, this study can be continued by testing the model empirically through distributing the questionnaire to some participant of SMEs and processing data from the results of questionnaire distribution using the data processing software like SPSS, LISRELL, etc

Delayed failure of software components using stochastic testing

The present research investigates the delayed failure of software components and addresses the problem that the conventional approach to software testing is unlikely to reveal this type of failure. Delayed failure is defined as a failure that occurs some time after the condition that causes the failure, and is a consequence of long-latency error propagation. This research seeks to close a perceived gap between academic research into software testing and industrial software testing practice by showing that stochastic testing can reveal delayed failure, and supporting this conclusion by a model of error propagation and failure that has been validated by experiment. The focus of the present research is on software components described by a request-response model. Within this conceptual framework, a Markov chain model of error propagation and failure is used to derive the expected delayed failure behaviour of software components. Results from an experimental study of delayed failure of DBMS software components MySQL and Oracle XE using stochastic testing with random generation of SQL are consistent with expected behaviour based on the Markov chain model. Metrics for failure delay and reliability are shown to depend on the characteristics of the chosen experimental profile. SQL mutation is used to generate negative as well as positive test profiles. There appear to be few systematic studies of delayed failure in the software engineering literature, and no studies of stochastic testing related to delayed failure of software components, or specifically to delayed failure of DBMS. Stochastic testing is shown to be an effective technique for revealing delayed failure of software components, as well as a suitable technique for reliability and robustness testing of software components. These results provide a deeper insight into the testing technique and should lead to further research. Stochastic testing could provide a dependability benchmark for component-based software engineering

An Empirical Investigation of the Key Factors for Refactoring Success in an Industrial Context

Refactoring is an increasingly practiced method in industrial software development. Stated simply, refactoring is an ongoing software improvement process that simplifies the internal structure of existing software, without changing its external behavior. The purpose is to improve the software and facilitate future maintenance and enhancement. Existing studies on refactoring mainly focus on its technical aspects and thus do not consider the team and human factors that influence its success. To identify the major facilitating factors for the success of refactoring, we interviewed 10 industrial software developers, and combined their responses with a study of the existing literature, formulated a model of refactoring success. The resulting conceptual model comprises both technical and non-technical factors. Technical factors include: level, testing and debugging, and tools, while the non-technical factors include: communication and coordination, support activities, individual capability/skills, and programmer participation. We propose to verify this model empirically through a survey of professional software developers (main body of refactoring practitioners). The survey design is provided

Mathematical modeling with digital technological tools for interpretation of contextual situations

This article has the goal of proposing physical contextual situations modeling as a way to interpret mathematical representations that are produced by digital technological tools. Thus, there is an experimental situation-problem about a physical phenomenon that is modeled through video analysis and dynamic geometry software; the methodological model Cuvima conducts the experimental activity. Pre-testing and post-testing measuring instruments were designed to obtain the information and previous conceptions of ten graduate students in Mathematical Education, which showed a conceptual change. Similarly, results prove that digital technology, from a didactical sequence, supports and strengthens experimental work simplifying modeling processes of a physical phenomenon, promoting the use of mathematical representations to solve a situation-problem

Factors Affecting the Adoption of Cloud for Software Development: A Case from Turkey

Cloud-based solutions for software development activities have been emerging in the last decade. This study aims to develop a hybrid technology adoption model for cloud use in software development activities. It is based on Technology Acceptance Model (TAM), Technology–Organization–Environment (TOE) framework, and the proposed extension Personal–Organization–Project (POP) structure. The methodology selected is a questionnaire-based survey and data are collected through personally administered questionnaire sessions with developers and managers, resulting in 268 responses regarding 84 software development projects from 30 organizations in Turkey, selected by considering company and project sizes and geographical proximity to allow face-to-face response collection. Structural Equation Modeling (SEM) is used for statistical evaluation and hypothesis testing. The final model was reached upon modifications and it was found to explain the intention to adopt and use the cloud for software development meaningfully. To the best of our knowledge, this is the first study to identify and understand factors that affect the intention of developing software on the cloud. The developed hybrid model was validated to be used in further technology adoption studies. Upon modifying the conceptual model and discovering new relations, a novel model is proposed to draw the relationships between the identified factors and the actual use, intention to use and perceived suitability. Practical and social implications are drawn from the results to help organizations and individuals make decisions on cloud adoption for software development

IN2GESOFT: Innovation and Integration of Methods for the Development and Quantitative Management of Software Projects TIN2004-06689-C03

This coordinated project intends to introduce new methods in software engineering project management, integrating different quantitative and qualitative technologies in the management processes. The underlying goal to all three subprojects participants is the generation of information adapted for the efficient performance in the directing of the project. The topics that are investigated are related to the capture of decisions in dynam ical environments and complex systems, software testing and the analysis of the manage ment strategies for the process assessment of the software in its different phases of the production. The project sets up a methodological, conceptual framework and supporting tools that facilitate the decision making in the software project management. This allows us to eval uate the risk and uncertainty associated to different alternatives of management before leading them to action. Thus, it is necessary to define a taxonomy of software models so that they reflect the current reality of the projects. Since the software testing is one of the most critical and costly processes directed to guarantee the quality and reliability of the software, we undertake the research on the automation of the process of software testing by means of the development of new technologies test case generation, mainly based in metaheuristic and model checking techniques in the domains of database and internet applications. The software system developed will allow the integration of these technologies, and the management information needed, from the first phases of the cycle of life in the construction of a software product up to the last ones such as regression tests and maintenance. The set of technologies that we investigate include the use of statistical analysis and of experimental design for obtaining metrics in the phase of analysis, the application of the bayesian nets to the decision processes, the application of the standards of process eval uation and quality models, the utilization of metaheuristics algorithms and technologies of prediction to optimize resources, the technologies of visualization to construct control dashboards, hybrid models for the simulation of processes and others

Towards a Model-Centric Software Testing Life Cycle for Early and Consistent Testing Activities

The constant improvement of the available computing power nowadays enables the accomplishment of more and more complex tasks. The resulting implicit increase in the complexity of hardware and software solutions for realizing the desired functionality requires a constant improvement of the development methods used. On the one hand over the last decades the percentage of agile development practices, as well as testdriven development increases. On the other hand, this trend results in the need to reduce the complexity with suitable methods. At this point, the concept of abstraction comes into play, which manifests itself in model-based approaches such as MDSD or MBT. The thesis is motivated by the fact that the earliest possible detection and elimination of faults has a significant influence on product costs. Therefore, a holistic approach is developed in the context of model-driven development, which allows applying testing already in early phases and especially on the model artifacts, i.e. it provides a shift left of the testing activities. To comprehensively address the complexity problem, a modelcentric software testing life cycle is developed that maps the process steps and artifacts of classical testing to the model-level. Therefore, the conceptual basis is first created by putting the available model artifacts of all domains into context. In particular, structural mappings are specified across the included domain-specific model artifacts to establish a sufficient basis for all the process steps of the life cycle. Besides, a flexible metamodel including operational semantics is developed, which enables experts to carry out an abstract test execution on the modellevel. Based on this, approaches for test case management, automated test case generation, evaluation of test cases, and quality verification of test cases are developed. In the context of test case management, a mechanism is realized that enables the selection, prioritization, and reduction of Test Model artifacts usable for test case generation. I.e. a targeted set of test cases is generated satisfying quality criteria like coverage at the model-level. These quality requirements are accomplished by using a mutation-based analysis of the identified test cases, which builds on the model basis. As the last step of the model-centered software testing life cycle two approaches are presented, allowing an abstract execution of the test cases in the model context through structural analysis and a form of model interpretation concerning data flow information. All the approaches for accomplishing the problem are placed in the context of related work, as well as examined for their feasibility by of a prototypical implementation within the Architecture And Analysis Framework. Subsequently, the described approaches and their concepts are evaluated by qualitative as well as quantitative evaluation. Moreover, case studies show the practical applicability of the approach

Software-in-the-loop applications for improved physical model tests of ocean renewable energy devices using artificial intelligence

Experimental research in laboratory is a necessary and useful method to explore the full potential of a device. Because it does not only require much less money than the prototype at sea test, it also provides more reliable results compared to numerical simulations. Hence, it is significantly vital to make accurate model tests of the concerned ocean renewable energy (ORE) devices possible. For this reason, this study for a PhD degree has been finished and a thesis, therefore, is produced. There is a need for a method to provide linear or nonlinear real-time power-take-off forces to the wave energy converting mechanism in the water during the experiment. More urgently, it is essential to overcome the discrepancy caused by following Froude-scaling law and Reynold-scaling law in the test of a model-scaled FOWT. Two applications for WECs and FOWTs are proposed separately, to meet the challenges.;Following the conceptual design of the software-in-the-loop (SIL) application for a WEC, an innovative generic platform, which can explicitly provide a real-time PTO damping force in terms of either linear or non-linear (at different scales) is developed and characterised by 1349 drop tests. Subsequent physical model tests of a OWSC WEC device are carried out. The power efficiency of the OWSC WEC device under different PTO strategies is then estimated based on the analysis of experimental results. The best linear damping in regular waves is driven by gaining 80 in the control function, while 160 for nonlinear PTO damping. Furthermore, it is revealed that nonlinear PTOs have no distinct advantage in the amount of electricity output, but can lead to better stability and broader damping range. Following the conceptual design of an AI-based hybrid testing application for a FOWT system, a prediction module of the rotor thrust is needed to be estimated and optimised in the first place. For this reason, a considerable amount of simulations under various conditions are carried out by fully-coupled computation software, and the results obtained are used to train an artificial intelligence structure. Then a prediction module which depends on five inputs, and gives one output rotor thrust, is estimated mathematically. The mathematical module is converted to the control function in the program in a controller to execute it in real-time tests. Therefore, the AI machine is sometimes referred to as the SIL application for FOWTs, which consists of a prediction module obtained by AI training, a controller, and the program in the controller. The AI machine is the key component to implement the AI-based real-time hybrid model (AIReaTHM) testing methodology.;As one of the highlights in the present study, the AIReaTHM testing rig is developed, and bench tests are carried out with a manoeuvrable motion simulator. The comprehensive testing results are analysed for three purposes: 1, validating the AIReaTHM testing methodology. 2, assessing the influences of wind speed, wind turbulence intensity, wave spectrum, input hydrodynamic motions on rotor thrust are reflected by the SIL application.3, evaluating the systematic uncertainty in the testing rig, which is to be compensated by further improving the testing system. The effect of the surge frequency, wave spectrum and wind models have on the targeted thrust is discussed. The time delay in the testing system is identified as within 0.1s, and the overall uncertainty from the testing rig is 5-15KN (the minimum rotor thrust is 508KN, hence the uncertainty is 0.98%-2.95% in percentage) when compared to the AI prediction.;The testing rig developed is further applied to a 1:73 model of a Hywind floating wind turbine. 4 testing campaigns are carried out, and 303 independent tests are conducted. Testing results with the real-time rotor thrust provided by the AI-based software-in-the-loop application are compared with the other three comparative testing patterns. They are tests with a constant rotor thrust, without any rotor thrust, with AI predicted rotor thrust but without wave inputs, and in only wave conditions respectively. The performance of the rotor thrust obtained by the AI prediction agrees well with the benchmark testing results. Then, the hydrodynamic responses of the model are compared among those four testing patterns, for both regular wave tests and irregular wave tests in terms of time histories, RAOs, statistical analysis, and spectral analysis. The RAOs of the model under three testing patterns are given for regular wave tests. The hydrodynamic response revealed that the AIReaTHM is better than applying a constant rotor thrust atop of the model, though further improvement is required to meet realistic response. In the final chapter, conclusions are drawn and original contribution of this PhD study is outlined. Besides, a few points concerning future work are addressed.Experimental research in laboratory is a necessary and useful method to explore the full potential of a device. Because it does not only require much less money than the prototype at sea test, it also provides more reliable results compared to numerical simulations. Hence, it is significantly vital to make accurate model tests of the concerned ocean renewable energy (ORE) devices possible. For this reason, this study for a PhD degree has been finished and a thesis, therefore, is produced. There is a need for a method to provide linear or nonlinear real-time power-take-off forces to the wave energy converting mechanism in the water during the experiment. More urgently, it is essential to overcome the discrepancy caused by following Froude-scaling law and Reynold-scaling law in the test of a model-scaled FOWT. Two applications for WECs and FOWTs are proposed separately, to meet the challenges.;Following the conceptual design of the software-in-the-loop (SIL) application for a WEC, an innovative generic platform, which can explicitly provide a real-time PTO damping force in terms of either linear or non-linear (at different scales) is developed and characterised by 1349 drop tests. Subsequent physical model tests of a OWSC WEC device are carried out. The power efficiency of the OWSC WEC device under different PTO strategies is then estimated based on the analysis of experimental results. The best linear damping in regular waves is driven by gaining 80 in the control function, while 160 for nonlinear PTO damping. Furthermore, it is revealed that nonlinear PTOs have no distinct advantage in the amount of electricity output, but can lead to better stability and broader damping range. Following the conceptual design of an AI-based hybrid testing application for a FOWT system, a prediction module of the rotor thrust is needed to be estimated and optimised in the first place. For this reason, a considerable amount of simulations under various conditions are carried out by fully-coupled computation software, and the results obtained are used to train an artificial intelligence structure. Then a prediction module which depends on five inputs, and gives one output rotor thrust, is estimated mathematically. The mathematical module is converted to the control function in the program in a controller to execute it in real-time tests. Therefore, the AI machine is sometimes referred to as the SIL application for FOWTs, which consists of a prediction module obtained by AI training, a controller, and the program in the controller. The AI machine is the key component to implement the AI-based real-time hybrid model (AIReaTHM) testing methodology.;As one of the highlights in the present study, the AIReaTHM testing rig is developed, and bench tests are carried out with a manoeuvrable motion simulator. The comprehensive testing results are analysed for three purposes: 1, validating the AIReaTHM testing methodology. 2, assessing the influences of wind speed, wind turbulence intensity, wave spectrum, input hydrodynamic motions on rotor thrust are reflected by the SIL application.3, evaluating the systematic uncertainty in the testing rig, which is to be compensated by further improving the testing system. The effect of the surge frequency, wave spectrum and wind models have on the targeted thrust is discussed. The time delay in the testing system is identified as within 0.1s, and the overall uncertainty from the testing rig is 5-15KN (the minimum rotor thrust is 508KN, hence the uncertainty is 0.98%-2.95% in percentage) when compared to the AI prediction.;The testing rig developed is further applied to a 1:73 model of a Hywind floating wind turbine. 4 testing campaigns are carried out, and 303 independent tests are conducted. Testing results with the real-time rotor thrust provided by the AI-based software-in-the-loop application are compared with the other three comparative testing patterns. They are tests with a constant rotor thrust, without any rotor thrust, with AI predicted rotor thrust but without wave inputs, and in only wave conditions respectively. The performance of the rotor thrust obtained by the AI prediction agrees well with the benchmark testing results. Then, the hydrodynamic responses of the model are compared among those four testing patterns, for both regular wave tests and irregular wave tests in terms of time histories, RAOs, statistical analysis, and spectral analysis. The RAOs of the model under three testing patterns are given for regular wave tests. The hydrodynamic response revealed that the AIReaTHM is better than applying a constant rotor thrust atop of the model, though further improvement is required to meet realistic response. In the final chapter, conclusions are drawn and original contribution of this PhD study is outlined. Besides, a few points concerning future work are addressed