Examining Organizational Implications of Innovations in Software Development: Agile and Simulation

Software development is a complex process involving stakeholders with divergent perspectives, skills, and responsibilities who must work together to create a software product of high quality. Problems such as miscommunications and misunderstandings among project stakeholders, especially between the IS and business functions, exist in software development. To help address these issues, innovative methods are being increasingly adopted such as the Agile software development methodology and software simulation. These two methods share the same goal of bringing stakeholders together to establish a common understanding so that the system can be built quicker and better than with traditional approaches. This dissertation, which consists of two essays, focuses on these two innovative methods of software development – Agile methodology and software simulation – and examines how they can be best applied and under what conditions they lead to positive outcomes. The first essay studies the introduction of the Agile methodology in a company steeped in the traditional Waterfall software development method. The essay reports on how the Agile methodology was integrated with the traditional software development process including an in-depth analysis of the organizational and project controller-controlee relationships before and after the Agile methodology implementation. We find that outcome control, which was the predominant control mechanism, used in the company’s Waterfall development process, gave way to a hybrid control mechanism that possesses attributes of emergent control while maintaining vestiges of some Waterfall-like outcome control mechanisms. In addition, we find that the IS function must relinquish some influence over software development resources with the introduction of the Agile method. Lessons learned from this case study point to the complexity of designing organizational and project control mechanisms during the transition from the Waterfall to an Agile approach.As much as innovations in software development methods improve the software creation process, the risk of failing to create a quality software product are heightened when requirements are misinterpreted. Recent innovations in requirements simulations provide stakeholders with an opportunity to see realistic simulations of the system before it is built to quickly reach a common understanding of the requirements. Hence, the second essay empirically examines how the use of software simulations with various degrees of realism can help mitigate project requirements risk including project novelty, data interdependence, system interdependence, requirements instability, and requirements diversity, leading to higher software product quality. Results suggest that simulation realism partially mediates the relationship between project requirement risk and software product quality indicating the importance of investing in highly realistic simulations in software project requirement risk mitigation.Overall, this dissertation sheds light on how software development managers can employ innovative methods such as an Agile method and software simulation to bring greater stakeholders unity and produce higher quality software products

CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification

Training deep learning models on medical datasets that perform well for all classes is a challenging task. It is often the case that a suboptimal performance is obtained on some classes due to the natural class imbalance issue that comes with medical data. An effective way to tackle this problem is by using targeted active learning, where we iteratively add data points to the training data that belong to the rare classes. However, existing active learning methods are ineffective in targeting rare classes in medical datasets. In this work, we propose Clinical (targeted aCtive Learning for ImbalaNced medICal imAge cLassification) a framework that uses submodular mutual information functions as acquisition functions to mine critical data points from rare classes. We apply our framework to a wide-array of medical imaging datasets on a variety of real-world class imbalance scenarios - namely, binary imbalance and long-tail imbalance. We show that Clinical outperforms the state-of-the-art active learning methods by acquiring a diverse set of data points that belong to the rare classes.Comment: Accepted to MICCAI 2022 MILLanD Worksho