As software systems evolve over time, the quality of its structure and code degrade unless developers regularly maintain it, requiring significant effort. Automated tools to help developers maintain software have been well-studied in the past.
In particular, software remodularisation tools focus on improving the code structure quality with minimal effort by suggesting changes to the developers to obtain an improved modularisation.
While there has been considerable research on automated software remodularisation, it often faces one or more of the following three shortcomings.
First, the approach is applied to small or medium-size codebases, raising the question of whether it scales to large codebases. Second, the results are not validated by the developers of these codebases. Last, the algorithm optimises only from a code quality metrics point of view, not considering the perspective and knowledge of developers. In this thesis, we propose an approach to capture developers' domain knowledge of a large-scale object-oriented codebase, which uses an NSGA-III algorithm to suggest remodularisations that improve code structure quality and adhere to developer knowledge. Additionally, the results of the algorithm are validated by the developers. The results in this thesis show that with little effort, the domain knowledge of developers can be captured and used to improve the suggestions made by the algorithm.

Amsterdam Airport Schiphol has 5 runways, each of which can be used for take-off or landing of aeroplanes. The weather heavily influences which runway configuration air traffic control might pick. Airport Forecasting Service (AFOS) predicts which configuration of runways works most efficiently given a set of expected weather conditions and the standard deviations of wind components. These standard deviations give the system an indication of the accuracy of the weather forecasts.

Currently, the KNMI (Royal Netherlands Meteorological Institute) is the only meteorological institute that provides these standard deviations along with the weather forecast. This raises the main research question of this report: Is it possible to make accurate enough estimations of the standard deviation of wind direction and wind speed using historical data and future weather expectations. Estimating these standard deviations has been researched with two different approaches: a statistical method approach and a machine learning approach.

Statistical Methods Four fitting methods have been researched in search of the best statistical model to estimate the standard deviation of wind direction and speed: the Maximum Likelihood Method (MLM) and three Least Square Method implementations of a Weibull, Minimum Weibull and Double Weibull distribution. The performance of aggregates on the outcome of these four methods was also researched. One case takes the minimum standard deviation of the four, the other takes the mean.

MLM not only performs the best but also performs most consistently of the four fitting methods. Taking into account aggregates, MLM is more consistent than the minimum method but the minimum method outperforms it. Neither of these methods managed to meet the success criteria.

Machine Learning In regards to machine learning, the problem of estimating the standard deviations of wind direction and wind speed is a regression problem. The following machine learning models have been researched for Estimatic: MLPN, LSTM RNN, ERNN and RBFN.

LSTM RNNs outperform MLPNs, RBFNs and ERNNs for both wind direction and speed standard deviation estimation. LSTM RNN performance did not meet the success criteria.

The research concludes that it is not possible to make accurate enough estimations of the standard deviation of wind components using the historical data and future weather expectations available for Amsterdam Airport Schiphol.