Advances in technologies like artificial intelligence and metaverse have led to a proliferation of software systems in business and everyday life. With this widespread penetration, the carbon emissions of software are rapidly growing as well, thereby negatively impacting the long-term sustainability of our environment. Hence, optimizing software from a sustainability standpoint becomes more crucial than ever. We believe that the adoption of automated tools that can identify energy-inefficient patterns in the code and guide appropriate refactoring can significantly assist in this optimization. In this extended abstract, we present an industry case study that evaluates the sustainability impact of refactoring energy -inefficient code patterns identified by automated software sustainability assessment tools for a large application. Preliminary results highlight a positive impact on the application's sustainability post-refactoring, leading to a 29% decrease in per-user per-month energy consumption.

With the climate crisis looming, engineering sustainable software systems become crucial to optimize resource utilization, minimize environmental impact, and foster a greener, more resilient digital ecosystem. For developers, getting access to automated tools that analyze code and suggest sustainability-related optimizations becomes extremely important from a learning and implementation perspective. However, there is currently a dearth of such tools due to the lack of standardized knowledge, which serves as the foundation of these tools. In this paper, we motivate the need for the development of a standard knowledge base of commonly occurring sustainability weaknesses in code, and propose an initial way of doing that. Furthermore, through preliminary experiments, we demonstrate why existing knowledge regarding software weaknesses cannot be re-tagged “as is” to sustainability without significant due diligence, thereby urging further explorations in this ecologically significant domain.

Sustainable software engineering has received a lot of attention in recent times, as we witness an ever-growing slice of energy use, for example, at data centers, as software systems utilize the underlying infrastructure. Characterizing servers for their energy use accurately without being intrusive, is therefore important to make sustainable software deployment choices. In this paper, we introduce ESAVE which is a machine learning-based approach that leverages a small set of hardware attributes to characterize a server or virtual machine’s energy usage across different levels of utilization. This is based upon an extensive exploration of multiple ML approaches, with a focus on a minimal set of required attributes, while showcasing good accuracy. Early validations show that ESAVE has only around 12% average prediction error, despite being non-intrusive.

While traditionally, software comprehension relies on approaches like reading through the code or looking at charts on screens, which are 2D mediums, there have been some recent approaches that advocate exploring 3D approaches like Augmented or Virtual Reality (AR/VR) to have a richer experience towards understanding software and its internal relationships. However, there is a dearth of objective studies that compare such 3D representations with their traditional 2D counterparts in the context of software comprehension. In this paper, we present an evaluation study to quantitatively and qualitatively compare 2D and 3D software representations with respect to typical comprehension tasks. For the 3D medium, we utilize an AR-based approach for 3D visualizations of a software system (XRaSE), while the 2D medium comprises of textual IDEs and 2D graph representations. The study, which has been conducted using 20 professional developers, shows that for most comprehension tasks, the developers perform much better using the 3D representation, especially in terms of velocity and recollection, while also displaying reduced cognitive load and better engagement.

For delivering high-quality artifacts within the budget and on schedule, software delivery teams ideally should have a holistic and in-process view of the current health and future trajectory of the project. However, such insights need to be at the right level of granularity and need to be derived typically from a heterogeneous project environment, in a way that helps development team members with their tasks at hand. Due to client mandates, software delivery project environments employ many disparate tools and teams tend to be distributed, thus making the relevant information retrieval, insight generation, and developer intelligence augmentation process fairly complex. In this paper, we discuss our journey in this area spanning across facets like software project modelling and new development metrics, studying developer priorities, adoption of new metrics, and different approaches of developer intelligence augmentation. Finally, we present our exploration of new immersive technologies for human-centered software engineering.

Modern software delivery happens in a geographically distributed environment and resembles like a supply chain - consists of various participants, involves various phases, needs adherence to multiple regulations and needs to maintain artifacts' integrity throughout the delivery phases. This shift in software development brings along with it several challenges ranging from communication of information/knowledge, coordination and control of teams, activities adhering to goals and policies and artifacts adhering to quality, visibility, and management. With the dispersion of centralized control over software delivery to autonomous delivery organizations, the variety of processes and tools used turns transparency into opacity as autonomous teams use different software processes, tools, and metrics, leading to issues like ineffective compliance monitoring, friction prone coordination, and lack of provenance, and thereby trust. In this paper, we present a delivery governance framework based on distributed ledger technology that uses a notion of `software telemetry' to record data from disparate delivery partners and enables compliance monitoring and adherence, provenance and traceability, transparency, and thereby trust.