Tool Support for Design Science Research—Towards a Software Ecosystem: A Report from a DESRIST 2017 Workshop

The information systems (IS) field contains a rich body of knowledge on approaches, methods, and frameworks that supports researchers in conducting design science research (DSR). It also contains some consensus about the key elements of DSR projects—such as problem identification, design, implementation, evaluation, and abstraction of design knowledge. Still, we lack any commonly accepted tools that address the needs of DSR scholars who seek to structure, manage, and present their projects. Indeed, DSR endeavors, which are often complex and multi-faceted in nature and involve various stakeholders (e.g., researchers, developers, practitioners, and others), require the support that such tools provide. Thus, to investigate the tools that DSR scholars actually need to effectively and efficiently perform their work, we conducted an open workshop with DSR scholars at the 2017 DESRIST conference in Karlsruhe, Germany, to debate 1) the general requirement categories of DSR tool support and 2) the more specific requirements. This paper reports on the results from this workshop. Specifically, we identify nine categories of requirements that fall into the three broad phases (pre-design, design, and post design) and that contribute to a software ecosystem for supporting DSR endeavors

Routines and representations at work - observing the architecture of conceptual design

routines, representations, artifacts, product development, workplace observation, evolutionary economics, chip manufacturing

Multilingual opinion mining

170 p.Cada día se genera gran cantidad de texto en diferentes medios online. Gran parte de ese texto contiene opiniones acerca de multitud de entidades, productos, servicios, etc. Dada la creciente necesidad de disponer de medios automatizados para analizar, procesar y explotar esa información, las técnicas de análisis de sentimiento han recibido gran cantidad de atención por parte de la industria y la comunidad científica durante la última década y media. No obstante, muchas de las técnicas empleadas suelen requerir de entrenamiento supervisado utilizando para ello ejemplos anotados manualmente, u otros recursos lingüísticos relacionados con un idioma o dominio de aplicación específicos. Esto limita la aplicación de este tipo de técnicas, ya que dicho recursos y ejemplos anotados no son sencillos de obtener. En esta tesis se explora una serie de métodos para realizar diversos análisis automáticos de texto en el marco del análisis de sentimiento, incluyendo la obtención automática de términos de un dominio, palabras que expresan opinión, polaridad del sentimiento de dichas palabras (positivas o negativas), etc. Finalmente se propone y se evalúa un método que combina representación continua de palabras (continuous word embeddings) y topic-modelling inspirado en la técnica de Latent Dirichlet Allocation (LDA), para obtener un sistema de análisis de sentimiento basado en aspectos (ABSA), que sólo necesita unas pocas palabras semilla para procesar textos de un idioma o dominio determinados. De este modo, la adaptación a otro idioma o dominio se reduce a la traducción de las palabras semilla correspondientes

PERSONALITY TRAITS AND TOURISM-PRENEURSHIP TENDENCY OF HOSPITALITY AND TOURISM MANAGEMENT UNDERGRADUATES

This paper examined the relationship between the big five personality traits and tourism-preneurship tendency of undergraduate students of hospitality and tourism management, University of Port Harcourt. The study used a structured questionnaire. Multiple regressions analysis that allows for exploration of the interrelationship among sets of variables were adopted for the analysis of data collected. The researchers carefully screen the data in terms of missing values, influential outliers, normality, and multicollinearity using statistical package for social science (SPSS) software version 23 before proceeding with the analysis. The result shows that there is a positive and significant correlation between the five dimensions of personality traits and tourism-prenueuriship tendency (TPI) in respect to hospitality and tourism management undergraduate students, University of Port Harcourt, Nigeria except for openness to experiences. The result further validates the proposed big-five personality traits and tourism-preneurship tendency model for hospitality and tourism management undergraduate students of the University of Port Harcourt. It also confirms contentiousness as making the strongest unique contribution to tourism-preneurship tendency of hospitality and tourism management undergraduate students, University of Port Harcourt, Choba, Nigeria. This study contributed to the body of knowledge in the tourism domain by authenticating the usage of the big five personality traits for predicting tourism-entrepreneurship tendency of hospitality and tourism management students in an emerging economy. Overall, this research contributes to knowledge of university student’s personality traits and their relations with tourism-entrepreneurship intention. It fills the gap of limited empirical studies on entrepreneurship and tourism management in emerging economies

On Learning Meaningful Assert Statements for Unit Test Cases

Software testing is an essential part of the software lifecycle and requires a substantial amount of time and effort. It has been estimated that software developers spend close to 50% of their time on testing the code they write. For these reasons, a long standing goal within the research community is to (partially) automate software testing. While several techniques and tools have been proposed to automatically generate test methods, recent work has criticized the quality and usefulness of the assert statements they generate. Therefore, we employ a Neural Machine Translation (NMT) based approach called Atlas(AuTomatic Learning of Assert Statements) to automatically generate meaningful assert statements for test methods. Given a test method and a focal method (i.e.,the main method under test), Atlas can predict a meaningful assert statement to assess the correctness of the focal method. We applied Atlas to thousands of test methods from GitHub projects and it was able to predict the exact assert statement manually written by developers in 31% of the cases when only considering the top-1 predicted assert. When considering the top-5 predicted assert statements, Atlas is able to predict exact matches in 50% of the cases. These promising results hint to the potential usefulness ofour approach as (i) a complement to automatic test case generation techniques, and (ii) a code completion support for developers, whocan benefit from the recommended assert statements while writing test code

Deep Learning Software Repositories

Bridging the abstraction gap between artifacts and concepts is the essence of software engineering (SE) research problems. SE researchers regularly use machine learning to bridge this gap, but there are three fundamental issues with traditional applications of machine learning in SE research. Traditional applications are too reliant on labeled data. They are too reliant on human intuition, and they are not capable of learning expressive yet efficient internal representations. Ultimately, SE research needs approaches that can automatically learn representations of massive, heterogeneous, datasets in situ, apply the learned features to a particular task and possibly transfer knowledge from task to task. Improvements in both computational power and the amount of memory in modern computer architectures have enabled new approaches to canonical machine learning tasks. Specifically, these architectural advances have enabled machines that are capable of learning deep, compositional representations of massive data depots. The rise of deep learning has ushered in tremendous advances in several fields. Given the complexity of software repositories, we presume deep learning has the potential to usher in new analytical frameworks and methodologies for SE research and the practical applications it reaches. This dissertation examines and enables deep learning algorithms in different SE contexts. We demonstrate that deep learners significantly outperform state-of-the-practice software language models at code suggestion on a Java corpus. Further, these deep learners for code suggestion automatically learn how to represent lexical elements. We use these representations to transmute source code into structures for detecting similar code fragments at different levels of granularity—without declaring features for how the source code is to be represented. Then we use our learning-based framework for encoding fragments to intelligently select and adapt statements in a codebase for automated program repair. In our work on code suggestion, code clone detection, and automated program repair, everything for representing lexical elements and code fragments is mined from the source code repository. Indeed, our work aims to move SE research from the art of feature engineering to the science of automated discovery

Text-based Sentiment Analysis and Music Emotion Recognition

Nowadays, with the expansion of social media, large amounts of user-generated texts like tweets, blog posts or product reviews are shared online. Sentiment polarity analysis of such texts has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. We also witness deep learning techniques becoming top performers on those types of tasks. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First of all, deep neural networks are data hungry. They need to be fed with datasets that are big in size, cleaned and preprocessed as well as properly labeled. Second, the modern natural language processing concept of word embeddings as a dense and distributed text feature representation solves sparsity and dimensionality problems of the traditional bag-of-words model. Still, there are various uncertainties regarding the use of word vectors: should they be generated from the same dataset that is used to train the model or it is better to source them from big and popular collections that work as generic text feature representations? Third, it is not easy for practitioners to find a simple and highly effective deep learning setup for various document lengths and types. Recurrent neural networks are weak with longer texts and optimal convolution-pooling combinations are not easily conceived. It is thus convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song datasets are created utilizing social tags. One of the research interests of Telecom Italia was the exploration of relations between music emotional stimulation and driving style. Consequently, a context-aware music recommender system that aims to enhance driving comfort and safety was also designed. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted. Word embeddings of different parameters were exercised and results revealed that their quality is influenced (mostly but not only) by the size of texts they were created from. When working with small text datasets, it is thus important to source word features from popular and generic word embedding collections. Regarding the third problem, a series of experiments involving convolutional and max-pooling neural layers were conducted. Various patterns relating text properties and network parameters with optimal classification accuracy were observed. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews. Given that labeled data are becoming the bottleneck of the current deep learning systems, a future research direction could be the exploration of various data programming possibilities for constructing even bigger labeled datasets. Investigation of feature-level or decision-level ensemble techniques in the context of deep neural networks could also be fruitful. Different feature types do usually represent complementary characteristics of data. Combining word embedding and traditional text features or utilizing recurrent networks on document splits and then aggregating the predictions could further increase prediction accuracy of such models