Leveraging contextual embeddings and self-attention neural networks with bi-attention for sentiment analysis

People express their opinions and views in different and often ambiguous ways, hence the meaning of their words is often not explicitly stated and frequently depends on the context. Therefore, it is difficult for machines to process and understand the information conveyed in human languages. This work addresses the problem of sentiment analysis (SA). We propose a simple yet comprehensive method which uses contextual embeddings and a self-attention mechanism to detect and classify sentiment. We perform experiments on reviews from different domains, as well as on languages from three different language families, including morphologically rich Polish and German. We show that our approach is on a par with state-of-the-art models or even outperforms them in several cases. Our work also demonstrates the superiority of models leveraging contextual embeddings. In sum, in this paper we make a step towards building a universal, multilingual sentiment classifier.Peer ReviewedPostprint (published version

Protocol and Tools for Conducting Agile Software Engineering Research in an Industrial-Academic Setting: A Preliminary Study

Conducting empirical research in software engineering industry is a process, and as such, it should be generalizable. The aim of this paper is to discuss how academic researchers may address some of the challenges they encounter during conducting empirical research in the software industry by means of a systematic and structured approach. The protocol developed in this paper should serve as a practical guide for researchers and help them with conducting empirical research in this complex environment.Comment: Accepted to CESI 2018 - International Workshop on Conducting Empirical Studies in Industry (in conjunction with ICSE 2018

Protocol and tools for conducting agile software engineering research in an industrial-academic setting: a preliminary study

Conducting empirical research in software engineering industry is a process, and as such, it should be generalizable. The aim of this paper is to discuss how academic researchers may address some of the challenges they encounter during conducting empirical research in the software industry by means of a systematic and structured approach. The protocol developed in this paper should serve as a practical guide for researchers and help them with conducting empirical research in this complex environment.Peer ReviewedPostprint (author's final draft

Continual lifelong learning in natural language processing: a survey

Continual learning (CL) aims to enable information systems to learn from a continuous data stream across time. However, it is difficult for existing deep learning architectures to learn a new task without largely forgetting previously acquired knowledge. Furthermore, CL is particularly challenging for language learning, as natural language is ambiguous: it is discrete, compositional, and its meaning is context-dependent. In this work, we look at the problem of CL through the lens of various NLP tasks. Our survey discusses major challenges in CL and current methods applied in neural network models. We also provide a critical review of the existing CL evaluation methods and datasets in NLP. Finally, we present our outlook on future research directions.This work is supported in part by the Catalan Agencia de Gestión de Ayudas Universitarias y de Investigación (AGAUR) through the FI PhD grant; the Spanish Ministerio de Ciencia e Innovación and by the Agencia Estatal de Investigación through the Ramón y Cajal grant and the project PCIN-2017-079; and by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 947657).Peer ReviewedPostprint (published version

Mining dependencies in large-scale Agile software development projects: A quantitative industry study

Context: Coordination in large-scale software development is critical yet difficult, as it faces the problem of dependency management and resolution. In this work, we focus on managing requirement dependencies that in Agile software development (ASD) come in the form of user stories. Objective: This work studies decisions of large-scale Agile teams regarding identification of dependencies between user stories. Our goal is to explain detection of dependencies through users’ behavior in large-scale, distributed projects. Method: We perform empirical evaluation on a large real-world dataset from an Agile software organization, provider of a leading software for Agile project management. We mine the usage data of the Agile Lifecycle Management (ALM) tool to extract large-scale development project data for more than 70 teams running over a five-year period. Results: Our results demonstrate that dependencies among user stories are not frequently observed (the problem affects around 10% of user stories), however, their implications on large-scale ASD are considerable. Dependencies have impact on software releases and increase work coordination complexity for members of different teams. Conclusion: Requirement dependencies undermine Agile teams’ autonomy and are difficult to manage at scale. We conclude that leveraging ALM monitoring data to automatically detect dependencies could help Agile teams address work coordination needs and manage risks related to dependencies in a timely manner.This work is supported in part by the Catalan Agencia de Gestión de Ayudas Universitarias y de Investigación (AGAUR) through the FI PhD grant and the project 2017 SGR 01694. The research is also partially supported by the Spanish Ministerio de Economía, Industria y Competitividad through the GENESIS project (grant TIN2016-79269-R).Peer ReviewedPostprint (author's final draft

Big data analytics in Agile software development: A systematic mapping study

© 2020 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Context: Over the last decade, Agile methods have changed the software development process in an unparalleled way and with the increasing popularity of Big Data, optimizing development cycles through data analytics is becoming a commodity. Objective: Although a myriad of research exists on software analytics as well as on Agile software development (ASD) practice on itself, there exists no systematic overview of the research done on ASD from a data analytics perspective. Therefore, the objective of this work is to make progress by linking ASD with Big Data analytics (BDA). Method: As the primary method to find relevant literature on the topic, we performed manual search and snowballing on papers published between 2011 and 2018. Results: In total, 65 primary studies were selected and analyzed. Our results show that BDA is employed throughout the whole ASD lifecycle. The results reveal that data-driven software development is focused on the following areas: code repository analytics, defects/bug fixing, testing, project management analytics, and application usage analytics. Conclusions: As BDA and ASD are fast-developing areas, improving the productivity of software development teams is one of the most important objectives BDA is facing in the industry. This study provides scholars with information about the state of software analytics research and the current trends as well as applications in the business environment. Whereas, thanks to this literature review, practitioners should be able to understand better how to obtain actionable insights from their software artifacts and on which aspects of data analytics to focus when investing in such initiatives.This work is supported in part by the Catalan Agencia de Gestión deAyudas Universitarias y de Investigación (AGAUR) through the FI Ph.D. grant. The research is also partially supported by the Spanish Ministerio de Economía, Industria y Competitividad through the GENESIS project (grant TIN2016-79269-R).Peer ReviewedPostprint (author's final draft