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.

As the world takes cognizance of AI's growing role in greenhouse gas(GHG) and carbon emissions, the focus of AI research & development is shifting towards inclusion of energy efficiency as another core metric. Sustainability, a core agenda for most organizations, is also being viewed as a core non-functional requirement in software engineering. A similar effort is being undertaken to extend sustainability principles to AI-based systems with focus on energy efficient training and inference techniques. But an important question arises, does there even exist any metrics or methods which can quantify adoption of “green” practices in the life cycle of AI-based systems? There is a huge gap which exists between the growing research corpus related to sustainable practices in AI research and its adoption at an industry scale. The goal of this work is to introduce a methodology and novel metric for assessing “greenness” of any AI-based system and its development process, based on energy efficient AI research and practices. The novel metric, termed as Green AI Quotient, would be a key step towards AI practitioner's Green AI journey. Empirical validation of our approach suggest that Green AI Quotient is able to encourage adoption and raise awareness regarding sustainable practices in AI lifecycle.

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.

Topic-specific social media forums such as Reddit have become popular platforms for users discussing health-related information as well as for scientific analysis of that information. Such discussions among users have been found to be effective in providing useful insights and assistance in many healthcare applications. This study focuses on one such application, where we utilize Reddit posts related to drug addiction and substance abuse, in order to help the addicted people. We observe some linguistic differences in the posts as users gradually move from the addicted stage to more and more advanced recovery stages. We then classify user-posts on Reddit as to be indicative of drug addiction or of different stages of recovery of the user. By annotating Reddit posts with the help of standard social and health psychology literature, we develop a Machine Learning classifier based on linguistic features, to classify the posts among different classes related to addiction and recovery. Finally, we identify addicted users having an intention to recover and develop a methodology for personalized mentor recommendation, whereby we identify potential mentors who are already in their advanced stage of recovery from usage of the same drugs as the target addicted user. To our knowledge, this work is the first attempt to utilize social media for helping addicted users having intention to recover with personalized mentor recommendations to facilitate their process of recovery.

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.

There has been a considerable shift in the way how software is built and delivered today. Most deployed software systems in modern times are created by (autonomous) distributed teams in heterogeneous environments making use of many artifacts, such as externally developed libraries, drawn from a variety of disparate sources. Stakeholders such as developers, managers, and clients across the software delivery value chain are interested in gaining insights such as how and why an artifact came to where it is, what other artifacts are related to it, and who else is using this. Software provenance encompasses the origins of artifacts, their evolution, and usage and is critical for comprehending, managing, decision-making, and analyzing software quality, processes, people, issues etc. In this paper, we propose an extensible framework based on standard provenance model specifications and blockchain technology for capturing, storing, exploring, and analyzing software provenance data. Our framework (i) enhances trustworthiness of provenance data (ii) uncovers non-trivial insights through inferences and reasoning, and (iii) enables interactive visualization of provenance insights. We demonstrate the utility of the proposed framework using open source project data.

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.

In globally distributed software delivery, autonomous teams (crowd workers, vendors etc.) work together to build complex software. One of the key challenges in such an environment is to ensure integrity of software as it crosses the teams' boundaries. For example, during globally distributed software development, vulnerable open source components should not get introduced, or code should not be inadvertently changed. To track such essential characteristics of software, we propose a notion of a composite identity of software. ShIFt - the Software Identity Framework can construct sub-identities based on various elements of a software such as the code itself, third party components, run-time configurations etc. These sub-identities are then combined to generate a composite identity of a software. The key contributions of this paper are (i) an approach to create composite software identity and detect integrity issues between two instances of software, (ii) identification of the cause that led to integrity discrepancies, and (iii) prescription of remediation measures to maintain the integrity of software in the global delivery environment. We further use a Blockchain system to store and assess software identity, and consequently maintain software integrity.

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.

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.

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.

In this extended abstract, we propose a conceptual framework that leverages distributed ledger technology and smart contracts to create a decentralized system to capture the occurrence of interesting development activities (e.g., a development build) and associated contextual data, and automatically audit and evaluate compliance to governance policies. Our hypothesis is that such a framework will facilitate easier sharing of information across all participants of a distributed development team, compliance evaluation and early mitigation actions, leading to greater visibility and compliance. Currently, the proof of concept we are working on is focused on sharing and compliance evaluation of the open-source components used in software development.

We have recently witnessed tremendous success of Machine Learning (ML) in practical applications. Computer vision, speech recognition and language translation have all seen a near human level performance. We expect, in the near future, most business applications will have some form of ML. However, testing such applications is extremely challenging and would be very expensive if we follow today's methodologies. In this work, we present an articulation of the challenges in testing ML based applications. We then present our solution approach, based on the concept of Metamorphic Testing, which aims to identify implementation bugs in ML based image classifiers. We have developed metamorphic relations for an application based on Support Vector Machine and a Deep Learning based application. Empirical validation showed that our approach was able to catch 71% of the implementation bugs in the ML applications.

Duplicate Bug Detection is the problem of identifying whether a newly reported bug is a duplicate of an existing bug in the system and retrieving the original or similar bugs from the past. This is required to avoid costly rediscovery and redundant work. In typical software projects, the number of duplicate bugs reported may run into the order of thousands, making it expensive in terms of cost and time for manual intervention. This makes the problem of duplicate or similar bug detection an important one in Software Engineering domain. However, an automated solution for the same is not quite accurate yet in practice, in spite of many reported approaches using various machine learning techniques. In this work, we propose a retrieval and classification model using Siamese Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) for accurate detection and retrieval of duplicate and similar bugs. We report an accuracy close to 90% and recall rate close to 80%, which makes possible the practical use of such a system. We describe our model in detail along with related discussions from the Deep Learning domain. By presenting the detailed experimental results, we illustrate the effectiveness of the model in practical systems, including for repositories for which supervised training data is not available.

Test automation involves the automatic execution of test scripts instead of being manually run. This significantly reduces the amount of manual effort needed and thus is of great interest to the software testing industry. There are two key problems in the existing tools and methods for test automation - a) Creating an automation test script is essentially a code development task, which most testers are not trained on; and b) the automation test script is seldom readable, making the task of maintenance an effort intensive process. We present the Accelerating Test Automation Platform (ATAP) which is aimed at making test automation accessible to non-programmers. ATAP allows the creation of an automation test script through a domain specific language based on English. The English-like test scripts are automatically converted to machine executable code using Selenium WebDriver. ATAP's English-like test script makes it easy for non-programmers to author. The functional flow of an ATAP script is easy to understand as well thus making maintenance simpler (you can understand the flow of the test script when you revisit it many months later). ATAP has been built around the Eclipse ecosystem and has been used in a real-life testing project. We present the details of the implementation of ATAP and the results from its usage in practice.

Ontology, the shared formal conceptualization of domain information, has been shown to have multiple applications in modeling, processing and understanding natural language text. In this work, we use distributed word vectors out of various recent language models from Deep Learning for semi-automated domain ontology creation for closed domains. We cover all major aspects of Domain Ontology Induction or Learning like concept identification, attribute identification, taxonomical and non-taxonomical relationship identification using the distributed word vectors. Preliminary results show that simple clustering based methods using distributed word vectors from these language models outperforms methods using models like LSI in ontology learning for closed domains.