Context: Software development is a complex socio-technical endeavor that involves coordinating different disciplines and skill sets. Practitioners experiment with and adopt processes and practices with a goal of making their work more effective. Objective: To observe, describe, and analyze software development processes and practices in an industrial setting. Our goal is to generate a descriptive theory of software engineering development, which is rooted in empirical data. Method: Following Constructivist Grounded Theory, we conducted a 2.5 year participant-observation of eight software projects at Pivotal, a software development company. We interviewed 33 software engineers, interaction designers, and product managers, and analyzed one year of retrospection topics. We iterated between data collection, data analysis and theoretical sampling until achieving theoretical saturation and generating a descriptive theory. Results: 1) This research introduces a descriptive theory of Sustainable Software Development. The theory encompasses principles, policies, and practices aiming at removing knowledge silos and improving code quality, hence leading to development sustainability. 2) At the heart of Sustainable Software Development is team code ownership. This research widens our understanding of team code ownership. Developers achieve higher team code ownership when they understand the system context, have contributed to the code in question, perceive code quality as high, believe the product will satisfy the user needs, and perceive high team cohesion. 3) This research introduces the first evidence-based waste taxonomy, identifying eight wastes along with causes and tensions, and compares it with Lean Software Development’s waste taxonomy. Conclusion: The Sustainable Software Development theory refines and extends our understanding of Extreme Programming by adding principles, policies, and practices (including Overlapping Pair Rotation) and aligning them with the business goal of sustainability. One key aspect of the theory is team code ownership, which is rooted in numerous cognitive, emotional, contextual and technical factors and cannot be achieved simply by policy. Another key dimension is waste identification and elimination, which has led to a new taxonomy of waste. Overall, this research contributes to the field of software engineering by providing new insights, rooted in empirical data, into how a software organization leverages and extends Extreme Programming to achieve software sustainability.

Sustainability study in the ever-growing Information technology (IT) sector is an emerging interdisciplinary research field. As one essential element in this sector, the development and implementation of cloud-based autonomous vehicles have the great potential to bring convenience to society and are defined as the climate change mitigation strategy. For instance, autonomous vehicles are able to fully utilize the eco-driving systems to reduce carbon emissions and reach high energy efficiency. Previous studies have shown that cloud IT service, one of the critical technologies for autonomous vehicles, is likely to yield novelties and advantages to the IT industry and reduce the greenhouse gas (GHG) emissions from other sectors. However, cloud services and their data center infrastructures consume plenty of electricity globally and cause GHG emission impacts. Robust methodologies to assess the environmental impacts related to cloud IT solutions are still lacking in academia and industry. In sum, there are knowledge gaps between empirical studies and general interest in software- supported and data-driven autonomous vehicles and their cloud service.  The purpose of this study is to investigate the possibilities and challenges connected to the assessment of the GHG impact related to cloud IT solutions in an autonomous vehicle set up. This study also aims to explore possible recommendations to reduce the GHG emission of cloud IT services. A qualitative in-depth case study is performed. The primary data is collected by semi-structured interview method, while the secondary data is collected by the scoping literature review method. The interviews are conducted with employees with different roles related to cloud services and/or sustainability at the case company.  The findings show the lack of transparent methodologies and calculation guidelines to assess cloud GHG emissions, both in the research community and industry. It shows the great opportunity and market demand for sound assessment methodologies and tools. Besides, six challenges to assessing cloud GHG emissions on the autonomous vehicle set up are identified: i) assessing system boundaries, ii) data quality and collection methods, iii) measurement methodologies, iv) calculation process, v) validation process, and vi) some other challenges. Additionally, five possible recommendations are developed to reduce the cloud GHG emissions: i) cloud GHG emission visualization and measurement tool, ii) better promotional schemes for user’s awareness and engagement, iii) investigations on both top-down and bottom-up approaches, iv) optimization through usage demand shaping, and v) optimization of the infrastructure services.