As Large Language Models (LLMs) become increasingly used for question-answering (QA), relying on static, pre-annotated references for evaluation poses significant challenges in cost, scalability, and completeness.Meanwhile, using LLMs themselves as evaluators without external grounding remains unreliable for objective tasks, as they systematically over-accept incorrect answers, fabricate supporting rationales, and degrade sharply on questions that fall outside their training data.We propose Search-AuGmented Evaluation (SAGE), a framework to assess LLM outputs without fixed groundtruth answers.Unlike conventional metrics that compare to static references or depend solely on LLM-as-a-judge knowledge, SAGE acts as an agent that actively retrieves and synthesizes external evidence.It iteratively generates web queries, collects information, summarizes findings, and refines subsequent searches through reflection.By reducing dependence on static reference-driven evaluation protocols, SAGE offers a scalable and adaptive alternative for evaluating the factuality of LLMs.Experimental results on multiple free-form QA benchmarks show that SAGE achieves substantial to perfect agreement with human evaluations.

Nowadays, software projects are a vital element in any organization’s success. It plays the highest role in the organization’s success in most cases. Hence, its main focus is to earn more money on a project and minimize the developing time cost. Sometimes due to unavailability of experts may decrease the profit of the organization. Thus, the organization comes with these types of issues. They want to increase the profit and decrease the development time and on fewer budgets, hire the more expert people to achieve the organization’s goal. For this purpose, they are applying the new development approach called GSD (global software development). Through global software development, they develop their project on a reasonable budget and maximize profit. However, in GSD, other challenges of team communication, coordination, geographical location, and cultural and time zone differences increase the project’s effort. This paper shows the more critical factors in GSD and the more challenging and shows their impact. They are more challenging on which project manager or team leader more focus on these factors, which can be more helpful for the project’s success. This paper is more helpful for both industries and researchers in that they can easily estimate the effort in the context of GSD.

Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers, and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assesses the ethical capabilities of AI systems and provides best practices for further improvements.

Software development processes play a key role in the software and system development life cycle. Processes are becoming complex and evolve rapidly due to the modern-day continuous software engineering (CSE) concepts, which are mainly based on continuous integration, continuous delivery, infrastructure-as-code, automation and more. The fast growing Chinese software development industry adopts various processes to achieve potential benefits offered in the international market. This study is conducted with the aim to investigate the trends of processes in practice in the Chinese industry. The survey questionnaire data is collected from 34 practitioners working in software development firms across the China and the results highlight that iterative and agile processes are extensively used in industrial setting. Furthermore, agile and traditional approaches are combined to develop the hybrid processes. Most of the participants are satisfied using the current development processes, however, they show interest to continuously improve the existing process models and methods. Finally, we noticed that majority of the software development organizations used the ISO 9001 standard for process assessment and improvement activities. The given results provide preliminary overview of processes deployed in the Chinese industry.

In recent years, the software release cost has been reduced dramatically due to the alteration from traditional shrink-wrapped software to software as a service. Organizations that can deliver their services continuously and with a high frequency have a higher ability to compete in the market. As a response to this, a substantial number of software companies acquired DevOps to establish a culture of effective communication and collaboration between development and operation teams and in order to enhance the production release frequency as well as to maintain the product quality. However, the DevOps environment requires a platform that aid in evaluating the performance of existing processes and provide improvement recommendations. On top of that, organizations can only achieve the perceived benefits of DevOps if their processes are mature and continuously measured. The objective of this research is to investigate the process improvement contributions made by researchers in the DevOps field. For this purpose, we performed a systematic literature review that resulted in several maturity models and best practices. Our ultimate aim is to develop a DevOps maturity model that can appraise and improve the processes in the DevOps environment.

Software fault prediction is a process to detect bugs in software projects. Fault prediction in software engineering has attracted much attention from the last decade. The early prognostication of faults in software minimize the cost and effort of errors that come at later stages. Different machine learning techniques have been utilized for fault prediction, that is proven to be utilizable. Despite, the significance of fault prediction most of the companies do not consider fault prediction in practice and do not build useful models due to lack of data or lack of enough data to strengthen the power of fault predictors. However, models trained and tested on less amount of data are difficult to generalize, because they do not consider project size, project differences, and features selection. To overcome these issues, we proposed an instance-based transfer learning through data leveraging using logistic linear regression as a base proposed statistical methodology. In our study, we considered three software projects within the same domain. Finally, we performed a comparative analysis of three different experiments for building models (targeted project). The experimental results of the proposed approach show promising improvements in (SFP).