Marta Olszewska

DevOps meets formal modelling in high-criticality complex systems

Quality is the cornerstone of high criticality systems, since in case of failure not only major financial losses are at stake, but also human lives. Formal methods that support model based-development are one of the methodologies used to achieve correct-by-construction systems. However, these are often heavy-weight and need a dedicated development process. In our work we combine formal and agile software engineering approaches. In particular, we use Event-B and Scrum to assure the quality and more rapid and flexible development. Since we identified that there are more prerequisites for a successful IT project, we use DevOps to embrace the development, quality assurance and IT operations. In this paper we show how formal modelling can function within DevOps and thus promote various dimensions of quality and continuous delivery.

Quantitatively measuring a large-scale agile transformation

We provide a quantitative metrics model to evaluate the impact of an agile and lean transformation.We propose eight rigorously described metrics within that model.We establish and apply the model in a large international telecommunication organization with 350 employees in two sites.Our findings show significant improvement in six of the eight metrics whereas one metric showed deteriorated results. Context: Agile software development continues to grow in popularity and is being adopted by more and more organizations. However, there is a need for empirical evidence on the impact, benefits and drawbacks of an agile transformation in an organization since the cost for such a transformation in terms of money, disrupted working routines and quality of development can become considerable. Currently, such evidence exists in the form of success stores and case studies, mostly of qualitative nature.Objective: Provide a metrics model to quantitatively measure the impact of an agile transformation in a software development organization.Method: The metrics model was elicited with the use of the Goal Question Metric approach.Results: A quantitative metrics model containing eight rigorously described metrics is presented and followed by its application to evaluate an agile and lean transformation in a large international telecommunication organization with 350 employees in two sites.Conclusions: The metrics model was sensitive to the changes that occurred in the organization and revealed significant improvements in six of the eight metrics and a deterioration in one of the metrics.

Tailoring complexity metrics for simulink models

The size and complexity of Simulink models is constantly increasing, just as the systems which they represent. Therefore, it is beneficial to control them already at the design phase. In this paper we establish a set of complexity metrics for Simulink models to capture diverse aspects of complexity by proposing new and redefining existing metrics. To evaluate the applicability of our metrics, we compare them with the closed-source metric proposed by Mathworks. Moreover, through a case study from the automotive domain, we relate such metrics to quality attributes as determined by domain experts, and correlate them to known faults. Preliminary assessment suggests that complexity is closely related to analysability, understandability, and testability.

Using Scrum to Develop a Formal Model – An Experience Report

The benefits of merging agile and formal methodologies have been discussed on the conceptual level for several years now, also in our previous work. This paper presents a hands-on investigation on the synergy of the Event-B formal method within the Scrum development process. A case study of the landing gear critical system is used to investigate the feasibility of such a merge. We provide the quantitative and qualitative analysis of the case study by measuring developer’s effort, size and complexity of the created model, as well as observing the development process. Our results show that the merge increases quality, in terms of sustaining the creation of correct and reliable systems (Event-B) and at the same time smoothens the modelling process, enhances comprehension of the system domain and requirements (Scrum).