Context: To ensure the success of software projects, it is essential to select skilled developers and to use suitable work contracts. Objective: This study tests two hypotheses: (i) the use of work-sample testing (trialsourcing) improves the selection of skilled software developers; and (ii) the use of contracts based on hourly payment leads to better software project outcomes than fixed-price contracts. Method: Fifty-seven software freelancers with relevant experience and good evaluation scores from previous clients were invited to complete a two-hour long trialsourcing task to qualify for a software development project. Thirty-six developers completed the trialsourcing task with acceptable performance, and, based on a stratified allocation process, were asked to give a proposal based on an hourly payment or a fixed-price contract. Eight hourly payment-based and eight fixed-priced proposals were accepted. The process and product characteristics of the completion of these 16 projects were collected and analysed. Results and Conclusion: Only partial support for our hypotheses was observed. While the use of trialsourcing may have prevented the selection of developers with insufficient skills, the performance on the trialsourcing task of the selected developers did not predict performance on the project. The use of hourly payments led to lower costs than fixed-price contracts, but not to improved processes or products. We plan to follow up these, to us unexpected, results with research on how to design more skill-predictive trialsourcing tasks, and when and why different project contexts give different contract consequences.

A general issue in climate science is to handle big data and run complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time and optimize data usage. The paper analyzes the effects of changes in land cover (LC), such as deforestation or urbanization, on local climate. Along with green house gas emission, LC changes are known to be important causes of climate change. ML methods were trained to learn the relation between LC changes and temperature changes. The results showed that Random Forest (RF) outperformed other ML methods. Explainable Artificial Intelligence (XAI) was further used to interpret the RF method and explain the impact of different LC changes on temperature. The results mainly agree with the climate science literature, demonstrating that ML methods in combination with XAI can be useful in analyzing the climate effects of LC changes. All parts of the analysis pipeline are explained including data pre-processing, feature extraction, ML training, performance evaluation and XAI.