Factors Affecting On-Time Delivery in Large-Scale Agile Software Development

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.

Automated summary

Late delivery and cost overruns are common problems in software projects, caused by insufficient effort estimation. Through surveys and data analysis, we identify factors such as requirements refinement, task dependencies, organizational alignment, and organizational politics as having significant impact on on-time delivery. Proxy measures like project size, number of dependencies, historical performance, and team familiarity help explain schedule deviations. We also discover hierarchical interactions among factors. Our findings are valuable for identifying and managing delay risks in agile settings, designing automated tools for predicting schedule overruns, and contributes to the development of a relational theory of software project management.