Software repositories are an essential source of information for software engineering research on topics such as project evolution and developer collaboration. Appropriate mining tools and analysis pipelines are therefore an indispensable precondition for many research activities. Ideally, valid results should not depend on technical details of data collection and processing. It is, however, widely acknowledged that mining pipelines are complex, with a multitude of implementation decisions made by tool authors based on their interests and assumptions. This raises the questions if (and to what extent) tools agree on their results and are interchangeable. In this study, we use two tools to extract and analyse ten large software projects, quantitatively and qualitatively comparing results and derived data to better understand this concern. We analyse discrepancies from a technical point of view, and adjust code and parametrisation to minimise replication differences. Our results indicate that despite similar trends, even simple metrics such as the numbers of commits and developers may differ by up to 500%. We find that such substantial differences are often caused by minor technical details. We show how tool-level and data post-processing changes can overcome these issues, but find they may require considerable efforts. We summarise identified causes in our lessons learned to help researchers and practitioners avoid common pitfalls, and reflect on implementation decisions and their influence in ensuring obtained data meets explicit and implicit expectations. Our findings lead us to hypothesise that similar uncertainties exist in other analysis tools, which may limit the validity of conclusions drawn in tool-centric research.

We report on a large-scale empirical study investigating the relevance of socio-technical congruence over key basic software quality metrics, namely, bugs and churn. In particular, we explore whether alignment or misalignment of social communication structures and technical dependencies in large software projects influences software quality. To this end, we have defined a quantitative and operational notion of socio-technical congruence, which we callsocio-technical motif congruence(STMC). STMC is a measure of the degree to which developers working on the same file or on two related files, need to communicate. As socio-technical congruence is a complex and multi-faceted phenomenon, the interpretability of the results is one of our main concerns, so we have employed a careful mixed-methods statistical analysis. In particular, we provide analyses with similar techniques as employed by seminal work in the field to ensure comparability of our results with the existing body of work. The major result of our study, based on an analysis of 25 large open-source projects, is that STMC isnotrelated to project quality measures—software bugs and churn—in any temporal scenario. That is, we find no statistical relationship between the alignment of developer tasks and developer communications on the one hand, and project outcomes on the other hand. We conclude that, wherefore congruence does matter as literature shows, then its measurable effect lies elsewhere.

Despite efforts in the security community to quickly index and disseminate vulnerabilities as they are discovered and addressed, there are concerns about how to scale up the knowledge management of vulnerabilities given its dramatic growth rate. To address these concerns, recent research shifted towards more proactive approaches, in particular leveraging text mining methods to improve vulnerability identification and dissemination to security investigators. While providing a starting point for understanding vulnerability trends, recent methods are still reliant on curated identifiers, such as 'CVE-*', hence missing the majority of cybersecurity activity. We show that we can leverage overlapping textual themes in software vulnerabilities to identify related software vulnerability discussions without prior knowledge of identifiers. Our method obtained 86% accuracy in identifying related vulnerabilities with minimal pre-processing in a noisy community.

Solar irradiance forecasting is an important problem in renewable energy management where any dips in solar energy generation must be made up for by reserves in order to ensure an uninterrupted energy supply. In this paper, we study several data mining methods for short term solar irradiance forecasting at a given location. In particular, we apply linear regression, probabilistic models, and naive Bayes classifier to forecast solar irradiance one day ahead, i.e., we forecast what tomorrow's solar irradiance will be like at sundown today. We evaluate the forecasting performance of our adaptations of the three models using land-based weather data from several weather stations on the island of Oahu in Hawai'i.

Context: The concept of code smells is widespread in Software Engineering. However, in spite of the many discussions and claims about them, there are few empirical studies to support or contest these ideas. In particular, the study of the human perception of what is a code smell and how to deal with it has been mostly neglected. Objective: To build empirical support to understand the effect of god classes, one of the most known code smells. In particular, this paper focuses on how conceptualization affects identification of god classes, i.e., how different people perceive the god class concept. Method: A controlled experiment that extends and builds upon another empirical study about how humans detect god classes [19]. Our study: i) deepens and details some of the research questions of the previous study, ii) introduces a new research question and, iii) when possible, compares the results of both studies. Result: Our findings show that participants have different personal criteria and preferences in choosing drivers to identify god classes. The agreement between participants is not high, which is in accordance with previous studies. Conclusion: This study contributes to expand the empirical data about the human perception of code smells. It also presents a new way to evaluate effort and distraction in experiments through the use of automatic logging of participant actions.