An Automated Framework for Detecting Change in the Source Code and Test Case Change Recommendation

Improvements and acceleration in software development have contributed towards high-quality services in all domains and all fields of industry, causing increasing demands for high-quality software developments. The industry is adopting human resources with high skills, advanced methodologies, and technologies to match the high-quality software development demands to accelerate the development life cycle. In the software development life cycle, one of the biggest challenges is the change management between the version of the source codes. Various reasons, such as changing the requirements or adapting available updates or technological upgrades, can cause the source code's version. The change management affects the correctness of the software service's release and the number of test cases. It is often observed that the development life cycle is delayed due to a lack of proper version control and due to repetitive testing iterations. Hence the demand for better version control-driven test case reduction methods cannot be ignored. The parallel research attempts propose several version control mechanisms. Nevertheless, most version controls are criticized for not contributing toward the test case generation of reduction. Henceforth, this work proposes a novel probabilistic rule-based test case reduction method to simplify the software development's testing and version control mechanism. Software developers highly adopt the refactoring process for making efficient changes such as code structure and functionality or applying changes in the requirements. This work demonstrates very high accuracy for change detection and management. This results in higher accuracy for test case reductions. The outcome of this work is to reduce the development time for the software to make the software development industry a better and more efficient world

The impact of immediate breast reconstruction on the time to delivery of adjuvant therapy: the iBRA-2 study

Background: Immediate breast reconstruction (IBR) is routinely offered to improve quality-of-life for women requiring mastectomy, but there are concerns that more complex surgery may delay adjuvant oncological treatments and compromise long-term outcomes. High-quality evidence is lacking. The iBRA-2 study aimed to investigate the impact of IBR on time to adjuvant therapy. Methods: Consecutive women undergoing mastectomy ± IBR for breast cancer July–December, 2016 were included. Patient demographics, operative, oncological and complication data were collected. Time from last definitive cancer surgery to first adjuvant treatment for patients undergoing mastectomy ± IBR were compared and risk factors associated with delays explored. Results: A total of 2540 patients were recruited from 76 centres; 1008 (39.7%) underwent IBR (implant-only [n = 675, 26.6%]; pedicled flaps [n = 105,4.1%] and free-flaps [n = 228, 8.9%]). Complications requiring re-admission or re-operation were significantly more common in patients undergoing IBR than those receiving mastectomy. Adjuvant chemotherapy or radiotherapy was required by 1235 (48.6%) patients. No clinically significant differences were seen in time to adjuvant therapy between patient groups but major complications irrespective of surgery received were significantly associated with treatment delays. Conclusion: IBR does not result in clinically significant delays to adjuvant therapy, but post-operative complications are associated with treatment delays. Strategies to minimise complications, including careful patient selection, are required to improve outcomes for patients

ETHICALLY PRACTICED UNETHICAL STRATEGIES IN PHARMA INDUSTRY - WHOM TO BE BLAMED

As the competition is getting more intense, the number of instances of companies alleged to have been involved in illegal and unethical practices is increasing at an alarming rate. Being an integral part of the society, business organizations have certain duties, responsibilities, and obligations toward the society, referred to as "Business Ethics". The pressures of the reality challenges the ethical frameworks traditionally followed by organizations. The global pharmaceutical industry is highly regulated, capital intensive, and driven by large research and development expenditures. Despite the pharmaceutical industry’s notable contributions to human progress, it is fraught with ethical challenges. This paper presents the ethically practiced unethical strategies that are followed in the industry referencing the case studies of mega corporations and concludes the need for “systematic training in ethics” for all the stakeholders and the need for ethical leadership in an organization.</jats:p

An Association of Education and Income with Convenient Payment Mode in the E-tail Industry during the COVID Pandemic

E-tailing is the process of selling the goods and services to the large number of customers in the smaller quantity through the internet platform. Indian e-tailing sector is showing a burgeoning growth during the COVID Pandemic. The main purpose of the study is to test the association of the demographic variables Education and Income with the customer’s preference towards the mode of payment. The scope of the study is confined with both the tangibles and the intangibles in horizontal e-tailers. The sampling frame of the study is the respondents those who have a previous experience in buying goods from the horizontal e-tailers. The purposive sampling technique was adapted to choose the respondents. The sample size was 620 respondents. The data was collected during COVID pandemic in India. The data analysis tool was the Chi-square test. The result from the data analysis indicates that both the education and income of the customers are related to the customer’s preference towards the convenient mode of payment during the online shopping. The suggestions and conclusions are discussed further in detail.</jats:p

A survey on medical data analysis

In this section, overview is proposed identified with the utilization of A priori-like calculations in clinical and organic examinations for finding regular arrangements of properties esteems in information and afterward extricating consistent standards as suggestion conditions between upsides of noticed and estimated ascribes and symptomatic boundary. Affiliation rule mining is a compelling information mining procedure that has been utilized generally in wellbeing informatics research directly from its presentation. Since wellbeing informatics has gotten a ton of consideration from scientists somewhat recently, and it has created different sub domains, so it is intriguing just as fundamental for survey the best in class wellbeing informatics research. As information revelation specialists and experts have applied a variety of information digging procedures for information extraction from wellbeing information, so the utilization of affiliation rule mining strategies to the wellbeing informatics space has been centred around and concentrated exhaustively in this review.</jats:p

An Experimental Analysis on Rough Set Mean, Median, Mode Method of Dependency Values for Feature Selection in Medical Databases

The problem of imperfect knowledge has been tackled for a long time by philosophers, logicians and mathematicians. Recently it became an important issue for scientists, particularly in the area of Artificial Intelligence. Their square measure several approaches to the matter of the way to perceive and manipulate imperfect information. The most successful approach is based on the rough set notion proposed by Z. Pawlak in the article [1]. The proposed method to find the quick reduct in medical data set using the roughest theory. This method has applied in many classification algorithms and find the measures to calculate the accuracy of this proposed method.</jats:p