Essential Oils Based Nano Formulations against Postharvest Fungal Rots

Postharvest phytopathogenic rot fungi affect the quality and quantity of perishable fruits and vegetables. About 30–40% peaches deteriorate annually after harvest in world whereas 40–50% losses are reported from Pakistan. Our research envisages importance of an eco-friendly plant essential oils based nano formulations as a management strategy against postharvest deteriorating fungal rots by enhancing their shelf-life and to attenuate reliance on synthetic fungicides. Plant essential oils mode of action against fungal postharvest rots is responsible of rupturing plasma membrane of fungal cell wall. The natural ripening process of perishable commodities does not get affected by the presence of antifungal packaging in the form of plant essential oil nano formulations as no significant alteration in weight loss of produce was recorded. Challenges in applying EOs for microbial suppression in postharvest systems include optimizing their positioning in commercial fruit storage containers. Several innovative approaches are analyzed in terms of work environment and implementation regarding disease management along with future perspectives in concerning field

A Review based on Active Research Areas in Mining Software Bug Repositories: Limitations and Possible Future Trends

Introduction/ Importance of Study: Bug repository mining is a crucial research area in software engineering, analyzing software change trends, defect prediction, and evolution. It involves developing methods and tools for mining repositories, providing essential data for bug management. Objective: The goal of this study is to analyze and synthesize recent trends in mining software bug repositories, providing valuable insights for future research and practical bug management. Novelty statement: Our research contributes novel insights into mining software repository techniques and approaches employed in specific tasks such as bug localization, triaging, and prediction, along with their limitations and possible future trends. Material and Method: This study presents a comprehensive survey that categorizes and synthesizes the current research within this field. This categorization is derived from an in-depth review of studies conducted over the past fifteen years, from 2010 to 2024. The survey is organized around three key dimensions: the test systems employed in bug repositories, the methodologies commonly used in this area of research, and the prevailing trends shaping the field. Results and Discussion: Our results highlight the significance of artificial intelligence and machine learning integration in bug repository mining; that has revolutionized software development process by enhancing classification, prediction and vulnerability detection of bugs. Concluding Remarks: This survey aims to provide a clear and detailed understanding of the evolution of bug repository mining, offering valuable insights for ongoing advancement of software engineering

A Review based on Active Research Areas in Mining Software Bug Repositories: Limitations and Possible Future Trends

Introduction/ Importance of Study: Bug repository mining is a crucial research area in software engineering, analyzing software change trends, defect prediction, and evolution. It involves developing methods and tools for mining repositories, providing essential data for bug management. Objective: The goal of this study is to analyze and synthesize recent trends in mining software bug repositories, providing valuable insights for future research and practical bug management. Novelty statement: Our research contributes novel insights into mining software repository techniques and approaches employed in specific tasks such as bug localization, triaging, and prediction, along with their limitations and possible future trends. Material and Method: This study presents a comprehensive survey that categorizes and synthesizes the current research within this field. This categorization is derived from an in-depth review of studies conducted over the past fifteen years, from 2010 to 2024. The survey is organized around three key dimensions: the test systems employed in bug repositories, the methodologies commonly used in this area of research, and the prevailing trends shaping the field. Results and Discussion: Our results highlight the significance of artificial intelligence and machine learning integration in bug repository mining; that has revolutionized software development process by enhancing classification, prediction and vulnerability detection of bugs. Concluding Remarks: This survey aims to provide a clear and detailed understanding of the evolution of bug repository mining, offering valuable insights for ongoing advancement of software engineering