Model Driven Development (MDD) requires proper tools to derive the implementation code from the application models. However, the integration of handwritten and generated code is a long-standing issue that affects the adoption of MDD in the industry. This article presents a model and code co-evolution approach that addresses such a problem a posteriori , using the standard collision detection capabilities of Version Control Systems to support the semi-automatic merge of the two types of code. We assess the proposed approach by contrasting it with the more traditional template-based, forward-engineering process, adopted by most MDD tools.

The wide range of different platforms for web and mobile applications development requires fast prototyping and the evaluation of multiple releases. Model Driven Development (MDD) represents the application at high level with textual or visual languages and can be used to automatically generate the final product by means of model-to-model and model-to-code transformations. We describe IFMLEdit.org, an open-source, online MDD tool for the specification and automatic generation of fast prototypes of Web and mobile application from IFML (Interaction Flow Modeling Language) specifications. The tool also supports the semantic mapping from IFML to PCN, a variant of Petri Nets, for model simulation and checking.

Despite the success of crowdsourcing marketplaces, fully harnessing their massive workforce remains challenging. In this work we study the effect on crowdsourcing campaigns of different feedback and payment strategies. Our results reveal the joint effect of feedback and payment on the quality and quantity of the outcome.

The possibility of assigning labels to localized regions in an image enables flexible image retrieval paradigms. However, the process of automatically segmenting and tagging images is notoriously hard, due to the presence of occlusions, noise, challenging illumination conditions, background clutter, etc. For this reason, human computation has recently emerged as a viable alternative when computer vision algorithms fail to provide a satisfactory answer. For example, Games with a purpose (GWAP) represent a powerful crowdsourcing mechanism to collect implicit annotations from human players. In this paper we consider the problem of aggregating the gaming tracks collected by a GWAP we developed to solve challenging instances of image segmentation problems. In particular we consider the existence of malicious players, who might try to fool the rules of the game to achieve higher rewards. The proposed solution can automatically estimate the reliability of human players, thus identifying cheaters. This information is exploited to aggregate the gaming tracks, thus significantly improving the image segmentation result and the quality of local image annotations.