Delays in the delivery of software projects and the corresponding cost and schedule overruns have been common problems in the software industry for years. A challenge within software project management is to make accurate effort estimations during planning. Software projects are complex networks, with multiple dependencies between software tasks.

This study aims to combine the field of effort estimation and networked classification to utilise network information for delay prediction in industry. We conducted a case study at ING, resulting in a number of insights with regards to networked classification in an industry setting.

There is a difference in the organisational structure of open-source and industry projects. This constitutes to a difference in available information, but there is also an opportunity to leverage the organisational structure of ING to improve delay prediction performance.

Using weights in networked classification has shown no improvement compared to not using them, but relational models do benefit from larger datasets as the used network contains more relational information.

Based on the insights we recommend ING to: keep track of more information, improve data quality by educating their teams and create models for specific domains or teams to leverage their organisational structure.

Intermax Cloudsourcing B.V. designs, implements and manages critical IT-infrastructures for Dutch clients from the medical, public and financial sectors. The information that passes over these IT-infrastructures is highly confidential and privacy-sensitive, therefore it is essential that these infrastructures are secure. To improve the security of their infrastructure, Intermax is developing a Security Operations Center (SOC) which is fed by information from an optical tap which is placed on one of Intermax's core routers. The goal of this project was to extend the SOC with a REST API that analyses and classifies SSL certificates to filter malicious network data. This REST API makes use of models, so in addition to the REST API itself, a Neural Network Framework has been built to create these models. The framework can be used for different sorts of network data, but for this project, a proof of concept using SSL certificates was worked out to provide Intermax with a working product. The SOC is being built upon the security analytics framework Apache Metron and the REST API will be incorporated using Metron's Model as a Service functionality. Apache Metron sends individual data packets to the REST API, which analyses the data packet and returns whether the packet is malicious or not with a certain accuracy. This analysis takes roughly 1 millisecond and is independent from other data packets. This allows Apache Metron to spawn multiple instances of the REST API, making the solution fast and scalable. The Neural Network Framework runs completely separate from Apache Metron and the REST API. A user can configure, train and test a neural network using the framework and their own dataset. The neural network can be stored on a storage medium. Consequently, the REST API can import and apply the neural network on incoming data.