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.

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.

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.

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.

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.

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.

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.