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Visual Programming Environments (VPEs) are predominantly being used to teach programming concepts through interactive games with interesting narratives. Games have been developed to teach basic concepts of programming such as deriving logic, writing code, debugging the code and so on. Debugging code is one of the most important activities that can improve the skill of tackling a problem. In programming, one needs to identify the correct location of an error and fix it, which is usually learned through experience. Games have been developed to teach debugging to novice programmers. Syntactical errors occur frequently in the early stages of programming. The existing debugging games aim to support users in debugging the logic of the problem, but do not target on correcting the code snippets based on syntax. To address this challenge of providing syntactical support, we propose a treasure hunt based debugging game, in which users pass through various levels of the game by debugging code snippets written in C language. We have evaluated G4D based on MEEGA+ model, with 20 volunteers, having different programming backgrounds. The results of the user survey indicate that G4D has a good quality level and about 75% of the volunteers have either strongly agreed or agreed to recommend G4D to their colleagues.

There is an ever increasing growth in the use of Q&A websites such as Stack Overflow (SO), so are the number of posts on them. These websites serve as knowledge sharing platforms where Subject Matter Experts (SMEs) and developers answer questions posted by other users. It is effort intensive for developers to navigate to right posts because of the large volume of posts on the platform, despite the presence of existing tags, that are based on technologies. Tagging these posts based on their context and purpose might help developers and SMEs in easily identifying questions they wish to answer and also in identifying contextually similar posts. To support this idea, we propose SOTagger as a prototype plug-in for Stack Overflow to tag questions contextually. We have considered SO data provided on SOTorrent and automated the identification of 6 categories of questions using Latent Dirichlet Allocation. We have also manually verified relevance of these categories. Using these categories and dataset, we have built a classification model to classify a post into one of these six categories using Support Vector Machine. We have evaluated SOTagger by conducting a user survey with 32 developers. The preliminary results are promising with about 80% developers recommending the plugin to others.