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.

Cloud storage services are often associated with various performance issues due to load imbalance, interference from background tasks such as data scrubbing, backfilling, recovery, and the difference in processing capabilities of heterogeneous servers in a datacenter. This has a significant impact on a broad range of applications that are characterized by massive working sets and real-time constraints. However, it is challenging and burdensome for human operators to hand-tune various control-knobs in a cloud-scale storage cluster for maintaining optimal performance under diverse workload conditions. Our study on an open-source object-based storage system, Ceph, shows that common load balancing strategies are ineffective unless they are adapted according to workload characteristics. Furthermore, positive effects of an applied strategy may not be immediately visible. To address these challenges, we developed a machine learning based system adaptation technique that enables a cloud storage system to manage itself through load balancing and data migration with the aim of delivering optimal performance in the face of diverse workload patterns and resource bottlenecks. In particular, we applied a stochastic policy gradient based reinforcement learning technique to track performance hotspots in the storage cluster, and take appropriate corrective actions to maximize future performance under a variety of complex scenarios. For this purpose, we leveraged system-level performance monitoring and commonly available control-knobs in object-based cloud storage systems. We implemented the developed techniques to build an Adaptive Resource Management (ARM) system for object based storage cluster, and evaluated its performance on NSF Cloud's Chameleon testbed. Experiments using Cloud Object Storage Benchmark (COSBench) show that, ARM improves the average read and write response time of Ceph storage cluster by upto 50% and 33% respectively, compared to the default case. It also outperforms a state-of-the-art dynamic load rebalancing technique in terms of read and write performance of Ceph storage by 43% and 36% respectively.

Large IT organizations depend on a global software delivery model which involves large teams with various roles and stakeholders. As software delivery is inherently collaborative, it requires the different roles to share artifacts, knowledge and insights with each other throughout the delivery life-cycle. Typically, each role has different insight and understanding needs, based on her/his context and activities. An architect may need to understand the system from a modularity perspective, a developer from code quality purposes, whereas a tester would need to understand it from a user-triggered flow perspective. However, the way we represent, understand, and collaborate on software artefacts, is still limited by the confines of traditional 2D computer screens. In this paper, we present our early work of an Extended Reality (XR) based approach that leverages affordances of natural human perception to represent and visualize software applications in three dimensions. This immersive approach is aimed at becoming a common fabric across global delivery roles, making activities like application comprehension, architecture analysis, knowledge communication, and analysis of a software's dynamic aspects, more contextual, richer, and intuitive. We posit that the use of XR, specifically augmented/mixed reality-based multidimensional views of different software artefacts, can be adapted to be an intuitive bridge across different roles, and foster a novel way of collaboration (both locally and globally). Here, we present our immersive approach and its prototype implementation, along with examples of its usage by different project roles. We also discuss some early feedback and the way forward.

Software engineering has seen much progress in recent past including introduction of new methodologies, new paradigms for software teams, and from smaller monolithic applications to complex, intricate, and distributed software applications. However, the way we represent, discuss, and collaborate on software applications throughout the software development life cycle is still primarily using the source code, textual representations, or charts on 2D computer screens - the confines of which have long limited how we visualize and comprehend software systems. In this paper, we present XRaSE, a novel prototype implementation that leverages augmented reality to visualize a software application as a virtually tangible entity. This immersive approach is aimed at making activities like application comprehension, architecture analysis, knowledge communication, and analysis of a software's dynamic aspects, more intuitive, richer and collaborative.

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.

Human activities can have a lasting impact on the environment and society. Environmental impact assessment (EIA) which focusses on evaluating the impact of proposed developmental projects on the environment, helps in transparent decision-making and involves multiple stakeholders. However, EIA is data and effort-intensive and often becomes complex and long-drawn. Moreover, EIA is currently performed using primarily two-dimensional traditional mediums which could be vastly restrictive and difficult to navigate and comprehend. Here, we present an immersive approach which can create 3D interactive elements, modelling the real-world using augmented/mixed reality. Because of the inherent benefits of using three-dimensional representations and associated real-world interactions, we posit that our approach will facilitate better and faster, collaboration-enabled analysis of a developmental project proposal, thereon reducing processing time and promoting high fidelity.

A large number of web applications are written using server-side scripting languages. Although web browsers allow clients to run these applications, it is often cumbersome to depend on desktops for the services provided by the applications. Given the popularity and convenience of mobile devices, there is a clear need to have native mobile apps driving the applications along with web browsers. In this paper, we present a solution to re-engineer web applications developed using server-side scripting languages, into native mobile apps. The solution takes source code of the web application along with its test suite as input and produces corresponding cross-platform mobile apps. The entire re-engineering process is fully automatic requiring no manual intervention at any stage. Our solution is generic enough not only to handle popular server-side scripting languages, but also to output mobile apps that support diverse popular platforms including Android, iOS, and Windows Mobile. To showcase the capability and generality of our solution, we have developed a prototype tool KIRKE to handle applications developed using JSP, PHP, and ASP.NET. We present three case studies based on real-life codebases to evaluate the correctness, coverage, usability, and performance of our solution. The results indicate that KIRKE is capable of generating a mobile app that preserves the functionality of original web application and uses resources more efficiently when compared to the web application running on a mobile browser.