Understanding, Analysis, and Handling of Software Architecture Erosion

Architecture erosion occurs when a software system's implemented architecture diverges from the intended architecture over time. Studies show erosion impacts development, maintenance, and evolution since it accumulates imperceptibly. Identifying early symptoms like architectural smells enables managing erosion through refactoring. However, research lacks comprehensive understanding of erosion, unclear which symptoms are most common, and lacks detection methods. This thesis establishes an erosion landscape, investigates symptoms, and proposes identification approaches. A mapping study covers erosion definitions, symptoms, causes, and consequences. Key findings: 1) "Architecture erosion" is the most used term, with four perspectives on definitions and respective symptom types. 2) Technical and non-technical reasons contribute to erosion, negatively impacting quality attributes. Practitioners can advocate addressing erosion to prevent failures. 3) Detection and correction approaches are categorized, with consistency and evolution-based approaches commonly mentioned.An empirical study explores practitioner perspectives through communities, surveys, and interviews. Findings reveal associated practices like code review and tools identify symptoms, while collected measures address erosion during implementation. Studying code review comments analyzes erosion in practice. One study reveals architectural violations, duplicate functionality, and cyclic dependencies are most frequent. Symptoms decreased over time, indicating increased stability. Most were addressed after review. A second study explores violation symptoms in four projects, identifying 10 categories. Refactoring and removing code address most violations, while some are disregarded.Machine learning classifiers using pre-trained word embeddings identify violation symptoms from code reviews. Key findings: 1) SVM with word2vec achieved highest performance. 2) fastText embeddings worked well. 3) 200-dimensional embeddings outperformed 100/300-dimensional. 4) Ensemble classifier improved performance. 5) Practitioners found results valuable, confirming potential.An automated recommendation system identifies qualified reviewers for violations using similarity detection on file paths and comments. Experiments show common methods perform well, outperforming a baseline approach. Sampling techniques impact recommendation performance

Possessions and the Self: Downstream Consequences of Ownership and Sharing What We Own

My dissertation is based on the premise that possessions are an extension of the self. Beyond simple functional benefits that possessions provide us, I question whether possessions affect our self-perception and behavior. Specifically, I focus on two aspects of possessions: Ownership (Essay 1) and Sharing (Essay 2). In Essay 1, I find that feeling a sense of product ownership has downstream consequences in one’s representation of who s/he is. Here I reveal that salient feelings of product ownership activate a product-related self in one’s mind, but more importantly deactivate product-unrelated self. By identifying simultaneous identity activation and deactivation, I show that an individual can only hold a limited number of salient selves, and activating one’s self aspect requires a trade-off. This finding updates the prior assumption in the literature that an individual can hold an unlimited number of selves, and further suggests that there is still a finite limit to what can be salient at a given time. My interest in ownership extends to Essay 2, where I examine another behavioral aspect of consumers: sharing. Sharing behavior has received much attention lately due to the rise of sharing economy platforms, which provide new opportunities for consumers to share personal belongings with others. In Essay 2, I mine people’s latent motivation behind sharing by using a transaction dataset from one of the largest sharing economy platforms, Airbnb. Here I find that people are driven by not only monetary, but also non-monetary reasons, such as desires to meet others and share the beauty of their homes. Then I explore how each motivation affects people’s engagement on the sharing economy platform and their continued effort to share. This second essay highlights individuals’ new role as micro-entrepreneurs in this new era of the 21st century

Personality extraction through LinkedIn

L'extraction de personnalité sur les réseaux sociaux est un domaine qui n'a que récemment commencé à capturer l'attention des chercheurs. La tâche consiste à, en partant d'un corpus de profils d'utilisateurs de réseaux sociaux, être capable de classifier leur personnalité correctement, selon un modèle de personnalité tel que défini en psychologie. Ce mémoire apporte trois innovations au domaine. Premièrement, la collecte d'un corpus d'utilisateurs LinkedIn. Deuxièmement, l'extraction sur deux modèles de personnalités, MBTI et DiSC, l'extraction sur DiSC n'ayant pas encore été faite dans le domaine, et finalement, la possibilité de passer d'un modèle de personnalité à l'autre est explorée, dans l'idée qu'il serait ainsi possible d'obtenir les résultats de multiples modèles de personnalités en partant d'un seul test.Personality extraction through social networks is a field that only recently started to capture the attention of researchers. The task consists in, starting with a corpus of user profiles on a particular social network, classifying their personalities correctly, according to a specific personality model as described in psychology. In this master thesis, three innovations to the domain are presented. Firstly, the collection of a corpus of LinkedIn users. Secondly, the extraction of the personality according to two personality models, DiSC and MBTI, the extraction with DiSC having never been done before. Lastly, the idea of going from one personality model to the other is explored, thus creating the possibility of having the results on two personality models with only one personality test

URLs can facilitate machine learning classification of news stories across languages and contexts

Comparative scholars studying political news content at scale face the challenge of addressing multiple languages. While many train individual supervised machine learning classifiers for each language, this is a costly and time-consuming process. We propose that instead of rely-ing on thematic labels generated by manual coding, researchers can use ‘distant’ labels created by cues in article URLs. Sections reflected in URLs (e.g., nytimes.com/politics/) can therefore help create training material for supervised machine learning classifiers. Using cues provided by news media organizations, such an approach allows for efficient political news identification at scale while facilitating imple-mentation across languages. Using a dataset of approximately 870,000 URLs of news-related content from four countries (Italy, Germany, Netherlands, and Poland), we test this method by providing a comparison to ‘classical’ supervised machine learning and a multilingual BERT model, across four news topics. Our results suggest that the use of URL section cues to distantly annotate texts provides a cheap and easy-to-implement way of classifying large volumes of news texts that can save researchers many valuable resources without having to sacrifice quality