Automatically Extracting Instances of Code Change Patterns with AST Analysis

A code change pattern represents a kind of recurrent modification in software. For instance, a known code change pattern consists of the change of the conditional expression of an if statement. Previous work has identified different change patterns. Complementary to the identification and definition of change patterns, the automatic extraction of pattern instances is essential to measure their empirical importance. For example, it enables one to count and compare the number of conditional expression changes in the history of different projects. In this paper we present a novel approach for search patterns instances from software history. Our technique is based on the analysis of Abstract Syntax Trees (AST) files within a given commit. We validate our approach by counting instances of 18 change patterns in 6 open-source Java projects.Comment: ICSM - 29th IEEE International Conference on Software Maintenance (2013

Discovering unbounded episodes in sequential data

One basic goal in the analysis of time-series data is to find frequent interesting episodes, i.e, collections of events occurring frequently together in the input sequence. Most widely-known work decide the interestingness of an episode from a fixed user-specified window width or interval, that bounds the subsequent sequential association rules. We present in this paper, a more intuitive definition that allows, in turn, interesting episodes to grow during the mining without any user-specified help. A convenient algorithm to efficiently discover the proposed unbounded episodes is also implemented. Experimental results confirm that our approach results useful and advantageous.Postprint (published version

Archaeological Quantification of Pottery: The Rims Count Adjusted using the Modulus of Rupture (MR)

Archaeological quantification is a recurrent issue in research about pottery, its typologies and its distribution. We accept the validity of other methods of quantification—sherd count, minimum number of individuals (MNI) or sherd weight—but the methodology that we have proposed for quantification of assemblages of archaeological contexts is the rims count, which has to be transformed into coefficients of reference through a correction using the modulus of rupture (MR). Such correctors are obtained through measuring the percentage of preserved rim of a significant number of sherds of each type and establishing the average of that percentage. This quantification method is easily applicable to all pottery types and it is also statistically reliable. Besides, it can be used in any study in which the gross number of rims is published. Finally, in the case of ceramic transport containers, a second correction can be applied by multiplying the corrected coefficient (number of rims × MR) by its average capacity (AC), another corrector that will allow us to gather statistics according to the litres of transported product. We believe that the rims count (the easiest part to classify) is a fast, relatively easy and very reliable method that needs to be corrected using the MR.This research has been developed in the context of the projects HAR2012-37003-C03-02, HAR2012-32881, HAR2011-28244, ARCHÉOSTRAITS and SIDPH/DI

Deep Memory Networks for Attitude Identification

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral. Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.Comment: Accepted to WSDM'1