Personality plays a significant role in our lives; it does not only influence what we think, feel, and do, but also affects what we say about what we think, feel, and do. In software engineering (SE), it might help in improving team composition through a combination of personalities within a team, and it could help explain work preferences and work satisfaction. Earlier studies in the field of software engineering have focused on extracting personality from developers with the use of questionnaires and automatic tools such as psycholinguistic tests. Psycholinguistic tests infer personality based on the words people use. As taking questionnaires is time-consuming, the interest in automated tools has grown. However, there is a lack of studies comparing different psycholinguistic models on actual SE data to validate to what extent these tools apply to software engineering. In this study, we compare two well-established academical models proposed by Yarkoni and Golbeck et al. and the popular industrial model Personality Insights by IBM. We use the three models to infer personality from comments on open-source projects on GitHub and compare the found scores to a ground-truth obtained through a questionnaire among software developers. In this study, we establish a baseline and compare three models on their performance to this baseline. We show three methods to perform almost equally when mean-centered, indicating the three methods may work on different scales. We show log-transformations to improve LIWC category scores found by reducing the effect of outliers and give recommendations for thirteen preprocessing steps to improve the inference on SE data. We found 600 to 1200 words per person to provide sufficient accuracy while remaining resource-aware and recommend a minimum of a hundred words for all three methods. Furthermore, we do not find enough evidence for discrimination by all three methods for people proficient in English compared to those who found themselves non-proficient in English. We find existing psycholinguistic models to be most useful for software engineering when used on a group or team level. When used on an individual level, one should take into account possible inaccuracies and consider the potentially harmful impact the misuse or misinterpretation of scores may have on an individual.

Through the years, companies have been exploring the field of data science. The Nederlandse Spoorwegen (NS) is not an exception to this. Modern trains are equipped with sensors that measure a variety of conditions within the train. This data is being stored in their data warehouse. This data has been proven useful for detection and response times to problems, which warrants two high-level goals of the NS: punctuality and reliability. However, even with the available data, visualization and detection of location-specific problems are not yet implemented. Location-specific problems are problems that are not caused by the train, but by the infrastructure or human fault at that specific location. At the moment, most patterns in error codes are only backed up by suspicions, since these error codes are not stored in a way they are easily readable. Therefore, it is hard to find connections between multiple error codes. This document describes the created system that supports the analysis of location-specific error code patterns. With the system, the NS will be able to improve their two high-level goals and ultimately improve customer satisfaction.

For the system, a framework was made, which allows the NS to further develop and extend on data analyses. Furthermore, an extensive UI was created, allowing users to investigate found error code patterns and trace back problems to their origin. With the system, the NS is able to verify and create new hypotheses on possible problematic locations. In this document, the problem in elaborated on, multiple solutions are given of which one is chosen and thoroughly motivated, the solutions are elaborated on and, finally, some recommendations for future expansion are given.