A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes

We propose a model to automatically describe changes introduced in the source code of a program using natural language. Our method receives as input a set of code commits, which contains both the modifications and message introduced by an user. These two modalities are used to train an encoder-decoder architecture. We evaluated our approach on twelve real world open source projects from four different programming languages. Quantitative and qualitative results showed that the proposed approach can generate feasible and semantically sound descriptions not only in standard in-project settings, but also in a cross-project setting.Comment: Accepted at ACL 201

Search-based model summarization

Large systems are complex and consist of numerous components and interactions between the components. Hence managing such large systems is a cumbersome and time consuming task. Large systems are usually described at the model level. But the large number of components in such models makes it difficult to modify. As a consequence, developers need a solution to rapidly detect which model components to revise. Effective solution is to generate a model summary. Although existing techniques are powerful enough to provide good summaries based on lexical information (relevant terms), they do not make use of structural information (component structure) well. In this thesis, model summarization is considered as an optimization problem that combines structural and lexical information to evaluate possible solutions. A summary solution is defined as a combination of model elements (e.g., classes, methods, comments, etc.) that should maximize, as much as possible, the coverage of both automatically generated structural rules and lexical information. The results of the experiments are reported on 6 open source projects where the majority of generated summaries are approved by developers --Abstract, page iii

Calculating the Upper Bounds for Multi-Document Summarization using Genetic Algorithms

Over the last years, several Multi-Document Summarization (MDS) methods have been presented in Document Understanding Conference (DUC), workshops. Since DUC01, several methods have been presented in approximately 268 publications of the stateof-the-art, that have allowed the continuous improvement of MDS, however in most works the upper bounds were unknowns. Recently, some works have been focused to calculate the best sentence combinations of a set of documents and in previous works we have been calculated the significance for single-document summarization task in DUC01 and DUC02 datasets. However, for MDS task has not performed an analysis of significance to rank the best multi-document summarization methods. In this paper, we describe a Genetic Algorithm-based method for calculating the best sentence combinations of DUC01 and DUC02 datasets in MDS through a Meta-document representation. Moreover, we have calculated three heuristics mentioned in several works of state-of-the-art to rank the most recent MDS methods, through the calculus of upper bounds and lower bounds