Abstract DRIVES is a 4‐year project created to deliver human capital solutions to the automotive supply chain and small‐ and medium‐sized enterprises (SMEs). The establishment of an Automotive Sector Skills Alliance covers all levels of the value chain (vehicle production, supply, sales, aftermarket services, and disposal). The automotive sector is facing many disruptive changes in this rapidly changing world. This paper, based on literature review, outlines the drivers of change that impact future developments in the automotive industry. This serves as an input to a questionnaire, which will provide a prioritisation and guidance to which direction the DRIVES project proposed and European Union (EU) skills agenda should concentrate.

Effort overruns is common problem in software development. Our main intention is to support estimation by method for classification of use cases. The goal of this paper is to evaluate usage of the feed-forward neural network for the Use Case classification purposes. Experimental results show that the feed-forward neural network classifier, using softmax activation function in the output layer and hyperbolic tangent activation function in the hidden layer, offers the best classification performance.

In this paper we present an algorithm for finding an optimal configuration of the artificial neural network that is used for the classification in our use case based effort estimation tool. This approach is based on feed-forward artificial neural network and is trained using the back-propagation training algorithm. Our goal is to find the optimal number of hidden neurons and the optimal number of training iterations to be able to reach maximal accuracy of neural network during the estimations. We demonstrate the usage of the proposed algorithm and its result on the estimation example that contains training and testing datasets of UseCases obtained from real software project development.

In the last decades we have got used to software applications and services being everywhere and working for us. However sometimes they fail to work as desired. The situation we experience nowadays is often characterized as the second software crisis and it is caused by many root causes. One main root problem is an old one – it is an insufficient specification of requirements and processes to be executed in software development. Even though the computer science offers many specification methods, standards, generic software processes, best practices, and languages, the problem is still here. The research presented in this work proposes utilization of formal methods and knowledge bases as a key solution for software process capture and modeling; the goal of the research is to describe particular sub-processes of software development process and their optimization with formalisms like Web Ontology Language (OWL). We will propose and develop complex software process modeling methodology that would combine semi-formal and formal approaches with forward and reverse process engineering (process mining) that would be used easily as well-known semi-formal approaches.

Many decisions during making a project estimation and planning are based on previous experience and competency of a manager. Evaluated completed projects provide rich knowledge about similar decisions and reality. This paper focuses on the approach how to estimate value of specific parameters of the projects utilizing parameterized use case model. The proven methods are used -- SOM for grouping similar use-cases and fuzzy rules for calculating the search parameter value. An important part of the approach there is to build up and maintain comparative database of valued use cases.

In this paper, we focus on the transformation of the functional view of the process model to the ontology description. Elements, their relations and attributes are described in the diagrams and then transformed to the ontology description. The transformation is bi-directional; it means that we can for example create the diagram of previously defined ontology description.