Explicating, Understanding and Managing Technical Debt from Self-Driving Miniature Car Projects

Technical debt refers to various weaknesses in the design or implementation of a system resulting from trade-offs during software development usually for a quick release. Accumulating such debt over time without reducing it can seriously hamper the reusability and maintainability of the software. The aim of this study is to understand the state of the technical debt in the development of self-driving miniature cars so that proper actions can be planned to reduce the debt to have more reusable and maintainable software. A case study on a selected feature from two self-driving miniature car development projects is performed to assess the technical debt. Additionally, an interview study is conducted involving the developers to relate the findings of the case study with the possible root causes. The result of the study indicates that "the lack of knowledge" is not the primary reason for the accumulation of technical debt from the selected code smells. The root causes are rather in factors like time pressure followed by issues related to software/hardware integration and incomplete refactoring as well as reuse of legacy, third party, or open source code

Ambidexterity in large-scale software engineering

Software is pervading our environment with products that become smarter and smarter every day. In order to follow this trend, software companies deliver continuously new features, in order to anticipate their competitors and to gain market share. For this reason, they need to adopt processes and organization solutions that allow them to deliver continuously. A key challenge for software organizations is to balance the resources in order to deliver enough new features in the short-term but also to support the delivery of new features in the long-term. In one word, companies need to be ambidextrous. In this thesis we investigate what ambidexterity is, what are the factors that hinder large software companies to be ambidextrous, and we provide initial solutions for the mitigation of such challenges. The research process consists of an empirical investigation based on the Grounded Theory approach, in which we conducted several case studies based on continuous interaction with 7 large software organizations developing embedded software. The results in this thesis are grounded in a large number of data collected, and corroborated by a combination of exploratory and confirmatory, as well as qualitative and quantitative data collection. The contributions of this thesis include a comprehensive understanding of the factors influencing ambidexterity, the current challenges and a proposed solution, CAFFEA. In particular, we found that three main challenges where hampering the achievement of ambidexterity for large software companies. The first one is the conflict between Agile Software Development and software reuse. The second one is the complexity of balancing short-term and long-term goals among a large number of stakeholders with different views and expertize. The third challenge is the risky tendency, in practice, of developing systems that does not sustain long-term delivery of new features: this is caused by the unbalanced focus on short-term deliveries rather than on the system architecture quality. This phenomenon is referred to as Architectural Technical Debt, which is a financial theoretical framework that relates the implementation of suboptimal architectural solutions to taking a debt. Even though such sub-optimal solutions might bring benefits in the short-term, a debt might have an interest associated with it, which consists of a negative impact on the ability of the software company to deliver new features in the long-term. If the interest becomes too costly, then the software company suffers delays and development crises. It is therefore important to avoid accumulation, in the system, of Architectural Technical Debt with a high interest associated with it. The solution proposed in this thesis is a comprehensive framework, CAFFEA, which includes the management of Architectural Technical Debt as a spanning activity (i.e., a practice shared by stakeholders belonging to different groups inside the organization). We have recognized and evaluated the strategic information required to manage Architectural Technical Debt. Then, we have developed an organizational framework, including roles, teams and practices, which are needed by the involved stakeholders. This solutions have been empirically developed and evaluated, and companies report initial benefits of applying the results in practice

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen