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.

Recent breakthroughs in Large Language Models (LLM), comprised of billions of parameters, have achieved the ability to unveil exceptional insight into a wide range of Natural Language Processing (NLP) tasks. The onus of the performance of these models lies in the sophistication and completeness of the input prompt. Minimizing the enhancement cycles of prompt with improvised keywords becomes critically important as it directly affects the time to market and cost of the developing solution. However, this process inevitably has a trade-off between the learning curve/proficiency of the user and completeness of the prompt, as generating such a solutions is an incremental process. In this paper, we have designed a novel solution and implemented it in the form of a plugin for Visual Studio Code IDE, which can optimize this trade-off, by learning the underlying prompt intent to enhance with keywords. This will tend to align with developers' collection of semantics while developing a secure code, ensuring parameter and local variable names, return expressions, simple pre and post-conditions. and basic control and data flow are met.

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.

Autonomous agents equipped with Large Language Models (LLMs) are rapidly gaining prominence as a revolutionary technology within the realm of Software Engineering. These intelligent and autonomous systems demonstrate the capacity to perform tasks and make independent decisions, leveraging their intrinsic reasoning and decision-making abilities. This paper delves into the current state of autonomous agents, their capabilities, challenges, and opportunities in Software Engineering practices. By employing different prompts (with or without context), we conclude the advantages of contextrich prompts for autonomous agents. Prompts with context enhance user requirement understanding, avoiding irrelevant details that could hinder task comprehension and degrade model performance, particularly when dealing with complex frameworks such as Spring Boot, Django, Flask, etc. This exploration is conducted using Auto-GPT (v0.3.0), an open-source application powered by GPT-3.5 and GPT-4 which intelligently connects the “thoughts” of Large Language Models (LLMs) to independently accomplish the assigned goals or tasks.

Energy efficiency has become an important consideration in deep learning systems. However, it remains a largely under-emphasized aspect during the development. Despite the emergence of energy-efficient deep learning patterns, their adoption remains a challenge due to limited awareness. To address this gap, we present DENT (Deep Learning Energy Pattern Tagger, a Chrome extension used to add "energy pattern tags" to the deep learning related questions from Stack Overflow. The idea of DENT is to hint to the developers about the possible energy-saving opportunities associated with the Stack Overflow post through energy pattern labels. We hope this will increase awareness about energy patterns in deep learning and improve their adoption. A preliminary evaluation of DENT achieved an average precision of 0.74, recall of 0.66, and an F1-score of 0.65 with an accuracy of 66%. The demonstration of the tool is available at https://youtu.be/S0Wf_w0xajw and the related artifacts are available at https://rishalab.github.io/DENT/

The exponential rise of deep learning, aided by the availability of several frameworks and specialized hardware, has led to its application in a wide variety of domains. The availability of GPUs has made it easier to train networks with a huge number of parameters. However, this rise has come at the expense of ever-increasing energy requirements and carbon footprint. While the existing work tries to combat this issue by proposing optimizations in the hardware and the neural network architectures, there is an absence of general energy efficiency guidelines for deep learning developers. In this paper, we propose an initial catalog of 8 energy patterns for developing deep learning applications by analyzing 1361 posts from Stack Overflow. Our hope is that these energy patterns may help the developers adopt energy efficient practices in their deep learning projects. A survey with 14 deep learning developers showed us that the developers are largely in agreement with the usefulness of the catalog from an energy efficiency perspective. A detailed description of the catalog, along with the posts related to each energy pattern, is available at the following link: https://rishalab.github.io/dl_energy_patterns/

Energy efficiency is an essential consideration in mobile application development, given that these apps run on battery-powered devices. This has led the researchers to develop a set of energy design patterns that can help the developers improve the energy efficiency of their applications. However, the adoption of these energy patterns in projects remains a challenge, given the lack of awareness about these patterns among the developers. To bridge this gap, we propose our tool eTagger, a Google Chrome extension that tags GitHub issues from Android repositories with associated energy patterns. eTagger works based on the embeddings generated by Sentence-BERT. We believe that labeling the GitHub issues with energy patterns may help towards their larger adoption as GitHub is a prominent platform in collaborative software development. A preliminary evaluation of eTagger achieved an AUC-ROC of 0.73 with a precision of 0.58, recall of 0.53 and an F1-score of 0.5. The demonstration of the tool is available at https://youtu.be/hP4pWJ4AKxE and related artifacts at https://rishalab.github.io/eTagger/.

Deploying applications on hybrid clouds with computational artifacts distributed over public backends and private edges involve several constraints. Designing such deployment requires application architects to solve several challenges, spanning over hard regulatory policy constraints as well as business policy constraints such as enablement of privacy by on-prem processing of data to the extent the business wants, backend support of privacy enabling technologies (PET), sustainability in terms of green energy utilization, latency sensitivity of the application. In this paper, we propose to optimize hybrid cloud application architectures, while taking all those factors into consideration, and empirically demonstrate the effectiveness of our approach. To the best of our knowledge, this work is the first of its kind.

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.

Data is a critical asset for organizations. It helps them generate business insights, improves decision making and creates a competitive advantage. Typically, organizations want exclusive control over data for their own advantage. To protect individual and national rights, governments frame data residency regulations. These laws govern the geographical constraints where storage, transmission and processing of data are allowed. Non-compliance to data regulations often lead to serious reper-cussions for organizations, ranging from hefty penalties to loss of brand value. The different variants of data residency constraints such as first copy within country storage poses challenges in designing a regulation-compliant application deployment architecture. In this paper, we propose a framework and multi-criteria decision technique for determining an optimal single cloud or multi cloud architecture. The framework is based on several criteria including permitted data flows as per regulations, data sensitivity and type, availability of cloud providers etc. The framework helps Cloud architects rapidly arrive at a set of deployment architecture options, which can further optimize by the architects.

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.

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.

Crowdsourced testing is gaining a lot of attention these days. Crowdsourced testing utilizes testers which subscribe to an external or internal crowdsourcing platform. Usually these testers are distributed across geographies. Thus, such testing can be treated as a form of distributed testing. Crowdsourced testing, quite often, is used to perform exploratory testing in which testers test the features as per their wish. Such testing results in redundant testing efforts due to the lack of awareness of other testers' activities, thus does not yield the full benefits of crowdsourced testing in terms of speed and coverage. Moreover, as each tester possesses a testing style, the present model of crowdsourced testing does not fully utilize tester's strength or style. In this paper, we study various redundancies involved in a distributed testing. We also study testers' behavior to understand various testing styles. The study finds that there exists a lot of redundancies in testing efforts. The study also observes that testers indeed have different testing styles which should be understood more deeply for engaging them better. Our study shows that there is need for tools and strategies for allocating testing tasks that leverages testers' testing style and creates more awareness among the testers.

Agile software development approaches are becoming mainstream as organizations recognize that their delivery methodology has to be nimble and flexible to accommodate new technologies and evolving customer requirements. However, large organizations depend on a global software delivery model wherein software teams are geographically distributed, and such an environment seems unsuited for Agile to succeed. In such scenarios, it is a challenge to be able to bring together the organization's Agile methodology, development environment, and distributed teams together in a standardized way, to be able to implement and govern the distributed delivery process objectively. Here, we present our approach to govern the adoption, usage and progress thereof of a distributed Agile methodology, that ties together the team and tool aspects with it. This becomes a single window to quickly bootstrap distributed Agile delivery projects using specific methods, metrics and dashboards, collaboration and gamification approaches. We have implemented this approach as an interactive Agile Workbench to present the teams and stakeholders with context-rich actionable alerts as well as situational awareness and helps bridge the gaps between cross-functional distributed teams which is essential to successful delivery of agile projects.

Applications have evolved significantly over time - from monolithic and self contained, to numerous plug gable apps available on various platforms these days. Modern applications their functionality as services in varying level of granularity and domains. This paradigm of Everything as a Service (XaaS), provides a dynamic environment wherein multiple smaller applications can be rapidly composed to create complex applications. Such composite applications would allow for efficient re-use of the existing applications and their services, instead of more traditional model of building everything from scratch. The intent of this paper is to demonstrate our initial work to implement an end-to-end delivery system in an enterprise scenario. We propose an automated algorithm to utilize a composer's input to match available services and create a composite plan or a manifest which is then used to quickly orchestrate the composite application in real-time. We have implemented our approach on our internal enterprise cloud using Puppet.

The last few years have seen two key trends maturing in the industry: IoT and Big Data. These trends build on more than a decade of research in both academia and industry. As the cost of instrumentation and microprocessor chips has declined, it is now possible to monitor the environment on a widening scale. The cost decline is matched by cloud computing (exposed as web services) that provides infrastructure for storage and processing. Furthermore, on top of cloud, advances in big data tools/techniques provide a platform to analyze and understand the massive amount of data. But data generated from IoT is expected not only to be Big (volume) but also Fast (velocity). Data analysis and machine learning will play a key role in unlocking the value generated by IoT data. The Internet of Things (IoT) can be thought of in terms of connecting and combining the above mentioned elements. Combination of all these elements at various levels (e.g., physical objects, cloud service, mobile with rich user interfaces, analytics) will allow access and analysis of an enormous amount of fast data, which could be used to improve efficiency and performance of the whole enterprise. Moreover, it opens the possibilities of developing IoT applications in novel scenarios such as smart metering, smart electric car recharge stations, retail & logistics, and so on. An important challenge that needs to be addressed is to enable the rapid development of IoT applications. Similar challenges have already been addressed in the closely related fields of Wireless Sensor and Actuator Networks (WSANs) and Pervasive/Ubiquitous computing. While the main challenge in the former is largely limited to similar nodes, the primary concern in the latter largely has been the heterogeneity of physical objects. The upcoming field of IoT will include both WSANs as well as heterogeneous physical objects, in addition to this it brings heterogeneity at various levels (e.g., physical objects, cloud services, smart phones with rich user interfaces, analytics). Therefore, Software Engineering (SE) support for IoT applications is needed to develop methodologies, abstractions, and techniques. Nevertheless, so far this topic has received very little attention by SE community.

As we move to the next generation of networks such as Internet of Things (IoT), the amount of data generated and stored on the cloud is going to increase by several orders of magnitude. Traditionally, storage or middleware layer encryption has been used for protecting data at rest. However, such mechanisms are not suitable for cloud databases. More sophisticated methods include user-layer-encryption (ULE) (where the encryption is performed at the end-user's browser) and application-layer-encryption (ALE) (where the encryption is done within the web-app). In this paper, we study security and functionality aspects of cloud encryption and present an ALE framework for Java called JADE that is designed to protect data in the event of a server compromise.

Knowledge Transition (KT) of legacy applications is a critical activity, often determining the quality of maintenance in the early stages of a maintenance life-cycle. We developed an integrated reverse engineering tool-suite that bootstraps the KT process by providing knowledge recipients insights to application structure, quality and functionality. The tool-suite is based on an in-depth study with KT practitioners and a comparative study of existing tools. We evaluated the benefits of the tool-suite during KT in real-life projects. In this talk, we report our learning from the study and evaluation phases.

Quality of requirements written in natural language has always been a critical concern in software engineering. Poorly written requirements lead to ambiguity and false interpretation in different phases of a software delivery project. Further, incomplete requirements lead to partial implementation of the desired system behavior. In this paper, we present a model for harvesting domain (functional or business) knowledge. Subsequently we present natural language processing and ontology based techniques for leveraging the model to analyze requirements quality and for requirements comprehension. The prototype also provides an advisory to business analysts so that the requirements can be aligned to the expected domain standard. The prototype developed is currently being used in practice, and the initial results are very encouraging.

Source code clustering is an important technique used in software development and maintenance to understand the modular structure of code. An array of algorithms are available for clustering like simulated annealing based search. Source code have different kinds of features such as structural or textual features. The collection of these different types of source code features and computation of relevant feature metrics is a difficult task. Further, the clustering algorithms can run on metrics based on different types of source code features or their combinations. This flexibility makes it non-trivial to test effectiveness of clustering algorithms on a source code. In this paper, we present a highly configurable clustering workbench that allows the user to collect the various source code features and then to select the code features used for clustering, the clustering algorithm and its various parameters. Clustering quality metrics are computed. They allow comparison of algorithm output based on different combinations of code-features and algorithms. We also present the specific contribution made in multi-dimensional feature analysis and clustering. The tool hides the algorithm complexity from the user, thus allowing complete focus on understanding the 'effect' of the configuration choices. We have also applied this tool in real-life maintenance projects, where the users found it useful to tweak the clustering techniques for the source-code peculiarities.

Cloud Computing is increasingly becoming popular as many enterprise applications and data are moving into cloud platforms. However, a major barrier for cloud adoption is real and perceived lack of security. In this paper, we take a holistic view of cloud computing security - spanning across the possible issues and vulnerabilities connected with virtualization infrastructure, software platform, identity management and access control, data integrity, confidentiality and privacy, physical and process security aspects, and legal compliance in cloud. We present our findings from the points of view of a cloud service provider, cloud consumer, and third-party authorities such as Govt. We also discuss important research directions in cloud security in areas such as Trusted Computing, Information Centric Security and Privacy Preserving Models. Finally, we sketch a set of steps that can be used, at a high level, to assess security preparedness for a business application to be migrated to cloud.