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A wide range of text-based artifacts contribute to software projects (e.g., source code, test cases, use cases, project requirements, interaction diagrams, etc.). Traceability Link Recovery (TLR) is the software task in which relevant documents in these various sets are linked to one another, uncovering information about the project that is not available when considering only the documents themselves...
We propose to study the impact of the representation of the data in defect prediction models. For this study, we focus on the use of developer activity data, from which we structure dependency graphs. Then, instead of manually generating features, such as network metrics, we propose a model inspired in recent advances in Representation Learning which are able to automatically learn representations...
Traditional affective lexicons are mainly based on discrete classes, such as positive, happiness, sadness, which may limit its expressive power compared to the dimensional representation in which affective meanings are expressed through continuous numerical values on multiple dimensions, such as valence-arousal. Traditional methods for acquiring dimensional lexicons are mainly based on time-consuming...
Predictive models for software projects' characteristics have been traditionally based on project-level metrics, employing only little developer-level information, or none at all. In this work we suggest novel metrics that capture temporal and semantic developer-level information collected on a per developer basis. To address the scalability challenges involved in computing these metrics for each...
One of the most interesting tasks in social network analysis is link prediction. There are a lot of studies dealing with link prediction task in the literature. In recent years, there is an increasing on link prediction methods trying to model network as more close to real networks such as heterogeneous, temporal and directed network models to gain better link prediction performance. Many of the existing...
1Success of Meetup groups is of utmost importance for the members who organize them. Given a wide variety of such groups, a single metric may not be indicative of success for different groups; rather, success measure should be specific to the interest of a group. In this paper, accounting for the group diversity, we systematically define Meetup group success metrics and use them to generate labels...
The process of clustering similar words is crucial for a broad range of applications such as text classification and word sense disambiguation. Several approaches for deriving word similarity have been proposed. Some, like latent semantic analysis, are derived from the distributional hypothesis. Others extract relationships between terms by drawing upon predefined linguistic patterns. In this work,...
Deep Convolutional Neural Networks(DCNNs) have recently shown great performance in many high-level vision tasks, such as image classification, object detection and more recently outdoor semantic segmentation. However, the convolutional layer only process the local regions in the image, ignoring the global context information. To overcome this poor localization property of Convolutional Neural Networks(CNNs),...
Context: Software source code is frequently changed for fixing revealed bugs. These bug-fixing changes might introduce unintended system behaviors, which are inconsistent with scenarios of existing regression test cases, and consequently break regression testing. For validating the quality of changes, regression testing is a required process before submitting changes during the development of software...
Reviews are text-based feedback provided by reviewers to authors. The quality of a review can be determined by identifying how relevant it is to the work that the review was written for as well as its similarity to existing well-written and coherent reviews. Relevance between two pieces of text can be determined by identifying semantic and syntactic similarities between them. In this paper, we make...
Many empirical studies have shown that defect prediction models built on product metrics can be used to assess the quality of software modules. So far, most methods proposed in this direction predict defects by class or file. In this paper, we propose a novel software defect prediction method based on functional clusters of programs to improve the performance, especially the effort-aware performance,...
Defects are unavoidable in software development and fixing them is costly and resource-intensive. To build defect prediction models, researchers have investigated a number of factors related to the defect-proneness of source code, such as code complexity, change complexity, or socio-technical factors. In this paper, we propose a new approach that emphasizes on technical concerns/functionality of a...
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