Empowering global software development with business intelligence

Context: Global Software Development (GSD) allows companies to take advantage of talent spread across the world. Most research has been focused on the development aspect. However, little if any attention has been paid to the management of GSD projects. Studies report a lack of adequate support for management’s decisions made during software development, further accentuated in GSD since information is scattered throughout multiple factories, stored in different formats and standards. Objective: This paper aims to improve GSD management by proposing a systematic method for adapting Business Intelligence techniques to software development environments. This would enhance the visibility of the development process and enable software managers to make informed decisions regarding how to proceed with GSD projects. Method: A combination of formal goal-modeling frameworks and data modeling techniques is used to elicitate the most relevant aspects to be measured by managers in GSD. The process is described in detail and applied to a real case study throughout the paper. A discussion regarding the generalisability of the method is presented afterwards. Results: The application of the approach generates an adapted BI framework tailored to software development according to the requirements posed by GSD managers. The resulting framework is capable of presenting previously inaccessible data through common and specific views and enabling data navigation according to the organization of software factories and projects in GSD. Conclusions: We can conclude that the proposed systematic approach allows us to successfully adapt Business Intelligence techniques to enhance GSD management beyond the information provided by traditional tools. The resulting framework is able to integrate and present the information in a single place, thereby enabling easy comparisons across multiple projects and factories and providing support for informed decisions in GSD management.

MDD vs. traditional software development: a practitioner’s subjective perspective

Context: Today’s project managers have a myriad of methods to choose from for the development of software applications. However, they lack empirical data about the character of these methods in terms of usefulness, ease of use or compatibility, all of these being relevant variables to assess the developer’s intention to use them. Objective: To compare three methods, each following a different paradigm (Model-Driven, Model-Based and Code-Centric) with respect to their adoption potential by junior software developers engaged in the development of the business layer of a Web 2.0 application. Method: We have conducted a quasi-experiment with 26 graduate students of the University of Alicante. The application developed was a Social Network, which was organized around a fixed set of modules. Three of them, similar in complexity, were used for the experiment. Subjects were asked to use a different method for each module, and then to answer a questionnaire that gathered their perceptions during such use. Results: The results show that the Model-Driven method is regarded as the most useful, although it is also considered the least compatible with previous developers’ experiences. They also show that junior software developers feel comfortable with the use of models, and that they are likely to use them if the models are accompanied by a Model-Driven development environment. Conclusions: Despite their relatively low level of compatibility, Model-Driven development methods seem to show a great potential for adoption. That said, however, further experimentation is needed to make it possible to generalize the results to a different population, different methods, other languages and tools, different domains or different application sizes.

Integrating the development of data mining and data warehouses via model-driven engineering

Data mining is one of the most important analysis techniques to automatically extract knowledge from large amount of data. Nowadays, data mining is based on low-level specifications of the employed techniques typically bounded to a specific analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Bearing in mind this situation, we propose a model-driven approach which is based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (that is deployed via data-warehousing technology) and the analysis models for data mining (tailored to a specific platform). Thus, analysts can concentrate on understanding the analysis problem via conceptual data-mining models instead of wasting efforts on low-level programming tasks related to the underlying-platform technical details. These time consuming tasks are now entrusted to the model-transformations scaffolding. The feasibility of our approach is shown by means of a hypothetical data-mining scenario where a time series analysis is required.

Towards a model-driven engineering approach of data mining

Nowadays, data mining is based on low-level specications of the employed techniques typically bounded to a specic analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Here, we propose a model-driven approach based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (via data-warehousing technology) and the analysis models for data mining (tailored to a specic platform). Thus, analysts can concentrate on the analysis problem via conceptual data-mining models instead of low-level programming tasks related to the underlying-platform technical details. These tasks are now entrusted to the model-transformations scaffolding.

Tasas de deformación GPS en la cuenca del Bajo Segura (Cordillera Bética oriental)

En este trabajo hemos cuantificado las tasas de deformación actual de la cuenca del Bajo Segura (NE del corredor de cizalla de la Bética oriental), a partir del análisis de una red GPS con 11 vértices geodésicos. Se han analizado los datos de cuatro campañas GPS entre junio de 1999 y enero de 2013, que han sido procesados con la versión 6.2 del software GIPSY-OASIS. Este software utiliza la técnica de posicionamiento puntual de precisión conocido por las siglas PPP. Se observa un acortamiento ~N-S en toda la cuenca de mayor magnitud en el sur, en la zona de falla del Bajo Segura, con valores que varían de oeste a este entre 0,73 y 0,24 mm/año. En el borde septentrional de la cuenca, en la zona de falla de Crevillente, los valores de acortamiento N-S son menores. Sin embargo, en esta falla se ha observado un movimiento lateral sinistrorso que, en la componente E-O, varía entre 0,44 y 0,75 mm/año.