Automatic program transformation tools can be valuable for programmers to help them with refactoring tasks, and for Computer Science students in the form of tutoring systems that suggest repairs to programming assignments. However, manually creating catalogs of transformations is complex and time-consuming. In this paper, we present REFAZER, a technique for automatically learning program transformations. REFAZER builds on the observation that code edits performed by developers can be used as input-output examples for learning program transformations. Example edits may share the same structure but involve different variables and subexpressions, which must be generalized in a transformation at the right level of abstraction. To learn transformations, REFAZER leverages state-of-the-art programming-by-example methodology using the following key components: (a) a novel domain-specific language (DSL) for describing program transformations, (b) domain-specific deductive algorithms for efficiently synthesizing transformations in the DSL, and (c) functions for ranking the synthesized transformations. We instantiate and evaluate REFAZER in two domains. First, given examples of code edits used by students to fix incorrect programming assignment submissions, we learn program transformations that can fix other students' submissions with similar faults. In our evaluation conducted on 4 programming tasks performed by 720 students, our technique helped to fix incorrect submissions for 87% of the students. In the second domain, we use repetitive code edits applied by developers to the same project to synthesize a program transformation that applies these edits to other locations in the code. In our evaluation conducted on 56 scenarios of repetitive edits taken from three large C# open-source projects, REFAZER learns the intended program transformation in 84% of the cases using only 2.9 examples on average.

Activity recognition has been widely studied in ubiquitous computing since it can be used in several application domains, such as fall detection and gesture recognition.Initially, works in this area were based on research-only devices (bodyworn sensors).However, with advances in mobile computing, current research focuses on mobile devices, mainly, smartphones.These devices provide Internet access, processing, and various sensors, such as accelerometer and gyroscope, which are useful resources for activity recognition.Therefore, many studies use smartphones as data source.Additionally, some works have already considered the use of wristbands and specially-designed watches, but fewer investigate the latest marketable wearable devices, such as smartwatches, which are less intrusive and can provide new opportunities to complement smartphone data.Moreover, for the best of our knowledge, no previous work experimentally evaluates the impact caused by the combination of sensor data from smartwatches and smartphones on the accuracy of activity recognition approaches.Therefore, the main goal of this experimental evaluation is to compare the use of data from smartphones as well as the combination of data from smartphones and smartwatches for activity recognition.We evidenced that the use of smartphone and smartwatch data combined can increase the accuracy of activity recognition.

While adding features, fixing bugs, or refactoring the code, developers may perform repetitive code edits. Although Integrated Development Environments (IDEs) automate some transformations such as renaming, many repetitive edits are performed manually, which is error-prone and time-consuming. To help developers to apply these edits, we propose a technique to perform repetitive edits using examples. The technique receives as input the source code before and after the developer edits some target locations of the change and produces as output the top-ranked program transformation that can be applied to edit the remaining target locations in the codebase. The technique uses a state-of-the-art program synthesis methodology and has three main components: a) a DSL for describing program transformations; b) synthesis algorithms to learn program transformations in this DSL; c) ranking algorithms to select the program transformation with the higher probability of performing the desired repetitive edit. In our preliminary evaluation, in a dataset of 59 repetitive edit cases taken from real C# source code repositories, the technique performed, in 83% of the cases, the intended transformation using only 2.8 examples.

Recommendation systems are software tools and techniques that provide customized content to users.The collaborative filtering is one of the most prominent approaches in the recommendation area.Among the collaborative algorithms, one of the most popular is the k-Nearest Neighbors (kNN) which is an instance-based learning method.The kNN generates recommendations based on the ratings of the most similar users (nearest neighbors) to the target one.Despite being quite effective, the algorithm performance drops while running on large datasets.We propose a method, called Restricted Space kNN that is based on the restriction of the neighbors search space through a fast and efficient heuristic.The heuristic builds the new search space from the most active users.As a result, we found that using only 15% of the original search space the proposed method generated recommendations almost as accurate as the standard kNN, but with almost 58% less running time.

Recommendation systems have been used in several application domains, most recently for TV (Digital TV, Smart TV, etc.).Several approaches can be used to recommend items, tags, etc., mainly based on user feedback.However, in the Digital TV domain, user feedback has to be done generally by using the remote control, which should be avoided to improve user experience, since assigning explicit feedback to items is restricted by the characteristics of this domain (difficulties when typing with the remote control, etc.).Moreover, in the Smart TV environment several types of items can be recommended (movies, musics, books, etc.).Thus, the recommendation should be generic enough to suit to different content.To solve the problem of acquiring explicit feedback and still generate personalized recommendations to be used by different Smart TV applications, this work proposes a recommendation architecture based on the extraction and classification of terms by analyzing the textual descriptions of TV programs present on electronic programming guides.In order to validate the proposed solution, a prototype using a real dataset has been developed, showing that using the recommended terms it is possible to generate final recommendations for different Smart TV applications.