Traceability for Model Driven, Software Product Line Engineering

Traceability is an important challenge for software organizations. This is true for traditional software development and even more so in new approaches that introduce more variety of artefacts such as Model Driven development or Software Product Lines. In this paper we look at some aspect of the interaction of Traceability, Model Driven development and Software Product Line

Quality of Web Mashups: A Systematic Mapping Study

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-04244-2_8Web mashups are a new generation of applications based on the composition of ready-to-use, heterogeneous components. They are gaining momentum thanks to their lightweight composition approach, which represents a new opportunity for companies to leverage on past investments in SOA, Web services, and public APIs. Although several studies are emerging in order to address mashup development, no systematic mapping studies have been reported on how quality issues are being addressed. This paper reports a systematic mapping study on which and how the quality of Web mashups has been addressed and how the product quality-aware approaches have been defined and validated. The aim of this study is to provide a background in which to appropriately develop future research activities. A total of 38 research papers have been included from an initial set of 187 papers. Our results provided some findings regarding how the most relevant product quality characteristics have been addressed in different artifacts and stages of the development process. They have also been useful to detect some research gaps, such as the need of more controlled experiments and more quality-aware mashup development proposals for other characteristics which being important for the Web domain have been neglected such as Usability and ReliabilityThis work is funded by the MULTIPLE project (TIN2009-13838), the Senescyt program (scholarships 2011), and the Erasmus Mundus Programme of the European Commission under the Transatlantic Partnership for Excellence in Engineering - TEE Project.Cedillo Orellana, IP.; Fernández Martínez, A.; Insfrán Pelozo, CE.; Abrahao Gonzales, SM. (2013). Quality of Web Mashups: A Systematic Mapping Study. En Current Trends in Web Engineering. Springer. 66-78. https://doi.org/10.1007/978-3-319-04244-2_8S6678Alkhalifa, E.: The Future of Enterprise Mashups. Business Insights. E-Strategies for Resource Management Systems (2009)Beemer, B., Gregg, D.: Mashups: A Literature Review and Classification Framework. Future Internet 1, 59–87 (2009)Cappiello, C., Daniel, F., Matera, M.: A Quality Model for Mashup Components. In: Gaedke, M., Grossniklaus, M., Díaz, O. (eds.) ICWE 2009. LNCS, vol. 5648, pp. 236–250. Springer, Heidelberg (2009)Cappiello, C., Daniel, F., Matera, M., Pautasso, C.: Information Quality in Mashups. IEEE Internet Computing 14(4), 32–40 (2010)Cappiello, C., Matera, M., Picozzi, M., Daniel, F., Fernandez, A.: Quality-Aware Mashup Composition: Issues, Techniques and Tools. In: 8th International Conference on the Quality of Information and Communications Technology (QUATIC 2012), pp. 10–19 (2012)Fenton, N.E., Pfleeger, S.L.: Software Metrics: A Rigorous and Practical Approach, 2nd edn. International Thompson 1996, pp. I–XII, 1–638 (1996) ISBN 978-1-85032-275-7Fernandez, A., Insfran, E., Abrahão, S.: Usability evaluation methods for the web: A systematic mapping study. Information and Software Technology 53(8), 789–817 (2011)Garousi, V., Mesbah, A., Betin-Can, A., Mirshokraie, S.: A systematic mapping study of web application testing. Information and Software Technology 55(8), 1374–1396 (2013)Grammel, L., Storey, M.-A.: A survey of mashup development environments. In: Chignell, M., Cordy, J., Ng, J., Yesha, Y. (eds.) The Smart Internet. LNCS, vol. 6400, pp. 137–151. Springer, Heidelberg (2010)Hoyer, V., Fischer, M.: Market Overview of Enterprise Mashup Tools. In: Bouguettaya, A., Krueger, I., Margaria, T. (eds.) ICSOC 2008. LNCS, vol. 5364, pp. 708–721. Springer, Heidelberg (2008)ISO/IEC: ISO/IEC 25010 Systems and software engineering. Systems and software Quality Requirements and Evaluation (SQuaRE). System and software quality models (2011)Kitchenham, B., Charters, S.: Guidelines for performing Systematic Literature Reviews in Software Engineering. Version 2.3, ESBE Technical Report, Keele University, UK (2007)Mendes, E.: A systematic review on the Web engineering research. In: International Symposium on Empirical Software Engineering (ISESE 2005), pp. 498–507 (2005)OrangeLabs: State of the Art in Mashup tools, SocEDA project, pp. 1–59 (2011)Petersen, K., Feldt, R., Mujtaba, S., Mattsson, M.: Systematic mapping studies in software engineering. In: 12th International Conference on Evaluation and Assessment in Software Engineering (EASE), pp. 68–77 (2008)Raza, M., Hussain, F.K., Chang, E.: A methodology for quality-based mashup of data sources. In: 10th International Conference on Information Integration and Web-based Applications & Services (iiWAS 2008), pp. 528–533 (2008)Saeed, A.: A Quality-based Framework for Leveraging the Process of Mashup Component Selection (2009), https://gupea.ub.gu.se/handle/2077/21953Sharma, A., Hellmann, T.D., Maurer, F.: Testing of Web Services - A Systematic Mapping. In: 8th World Congress on Services (SERVICES 2012), pp. 346–352 (2012

ASME 88-1CE-6, presented at the Energy-Source Technology Conference and Exhibition

Fig. 5 Velocity versus anguiar dispiacement (V8 engine) attained from the inertia value using the least squares method is consistently smaller than the reference data, and eventually leads to larger velocity estimation error than the average method Some precautions are needed when applying the least squares method to compute the engine inertia value. For engines operating at high speeds, the velocity related term in Eq. (1) could be very large compared with the other terms. This could result in some confusing situations. For instance, engines might decelerate over some portion of the engine operation cycle while the net external torque accelerating the engine is positive; or engines might accelerate while the net external torque is negative. These operation situations might make negative engine inertia value estimations possible, which is not feasible. In other cases, engines might have very small accelerations or decelerations while net external torque is moderate to large. For these cases, the calculation might lead to very large engine inertia values, which is not feasible either. The cases mentioned above are most likely to occur when engines operate at high speeds. Those erroneous data corresponding the situations above must be Altered out before applying the least squares method to the engine inertia value computation. The criterion used in this study to decide whether data should be used to calculate the engine inertia values is to check the quotient of the net external torque divided by the engine acceleration. This quotient should not be too large or too small relative to the average engine inertia value. Those data whose quotient are significantly away from the average engine inertia value are likely to fall in the situations mentioned above, and those data should not be used in the engine inertia value computation. V Conclusions The engine inertia values calculated by the least squares method guarantees minimum acceleration and velocity estimation errors for engine operating at constant average velocities. As for monotonically accelerating and decelerating engines, simulations in the study show that the engine model with an inertia calculated by the least squares method leads to smaller estimation errors in acceleration but larger estimation errors in velocity than the constant inertia engine model with an average inertia. It is important that the user knows the type of engine, its range of operation, and the type of loading in order to calculate an optimal engine inertia for the control purpose. This study has provided guidance in understanding the effects of engine performance variables and in calculating an appropriate estimate for the engine inertia. Acknowledgment

Considering agency and data granularity in the design of visualization tools

The Ecuadorian Government supports Gonzalo Gabriel Méndez through a SENESCYT scholarship.Previous research has identified trade-offs when it comes to designing visualization tools. While constructive “bottom-up” tools promote a hands-on, user-driven design process that enables a deep understanding and control of the visual mapping, automated tools are more efficient and allow people to rapidly explore complex alternative designs, often at the cost of transparency. We investigate how to design visualization tools that support a user-driven, transparent design process while enabling efficiency and automation, through a series of design workshops that looked at how both visualization experts and novices approach this problem. Participants produced a variety of solutions that range from example-based approaches expanding constructive visualization to solutions in which the visualization tool infers solutions on behalf of the designer, e.g., based on data attributes. On a higher level, these findings highlight agency and granularity as dimensions that can guide the design of visualization tools in this space.Postprin