Late delivery of software projects and cost overruns have been common problems in the software industry for decades. Both problems are manifestations of deficiencies in effort estimation during project planning. With software projects being complex socio-technical systems, a large pool of factors can affect effort estimation and on-time delivery. To identify the most relevant factors and their interactions affecting schedule deviations in large-scale agile software development, we conducted a mixed-methods case study at ING: two rounds of surveys revealed a multitude of organizational, people, process, project and technical factors which were then quantified and statistically modeled using software repository data from 185 teams. We find that factors such as requirements refinement, task dependencies, organizational alignment and organizational politics are perceived to have the greatest impact on on-time delivery, whereas proxy measures such as project size, number of dependencies, historical delivery performance and team familiarity can help explain a large degree of schedule deviations. We also discover hierarchical interactions among factors: organizational factors are perceived to interact with people factors, which in turn impact technical factors. We compose our findings in the form of a conceptual framework representing influential factors and their relationships to on-time delivery. Our results can help practitioners identify and manage delay risks in agile settings, can inform the design of automated tools to predict schedule overruns and can contribute towards the development of a relational theory of software project management.@en

Modern agile software projects are subject to constant change, making it essential to re-asses overall delay risk throughout the project life cycle. Existing effort estimation models are static and not able to incorporate changes occurring during project execution. In this paper, we propose a dynamic model for continuously predicting overall delay using delay patterns and Bayesian modeling. The model incorporates the context of the project phase and learns from changes in team performance over time. We apply the approach to real-world data from 4,040 epics and 270 teams at ING. An empirical evaluation of our approach and comparison to the state-of-the-art demonstrate significant improvements in predictive accuracy. The dynamic model consistently outperforms static approaches and the state-of-the-art, even during early project phases.@en

The development of Cloud Infra-Services has shifted over the past decade in the direction of a software code development process, also known as infrastructure as code (IaC). Contemporary continuous delivery settings in industry require fast feedback. As a consequence, companies need insight in time spent, especially in the development of such services. We examine a series of 28 Cloud Infra-Services within ING, and explore which factors affect their overall time to market and development time. An initial perception among several stakeholders in the Cloud Infra-Service development process, that Cloud Infra-Services within ING take longer than those in peer companies, is not confirmed by our benchmark. Development team members identified the time to internal market of services to be affected negatively by the portal where consumers can order a service and the Orchestration Workflows and by team dynamics. This perception is supported by additional metrics. We propose that promising ways to reduce lead time include reducing the complexity of the ING environment, by treating Cloud Infra-Services like regular software deliveries and by reducing the dependencies between teams in terms of tooling and collaboration.@en