In recent years,automation has become a key focus in software development as organizations seek to improve efficiency and reduce time-to-market.The integration of artificial intelligence(AI)tools,particularly those us...In recent years,automation has become a key focus in software development as organizations seek to improve efficiency and reduce time-to-market.The integration of artificial intelligence(AI)tools,particularly those using natural language processing(NLP)like ChatGPT,has opened new possibilities for automating various stages of the development lifecycle.The primary objective of this study is to evaluate the effectiveness of ChatGPT in automating various phases of software development.An artificial intelligence(AI)tool was developed using the OpenAI—Application Programming Interface(API),incorporating two key functionalities:1)generating user stories based on case or process inputs,and 2)estimating the effort required to execute each user story.Additionally,ChatGPT was employed to generate application code.The AI tool was tested in three case studies,each explored under two different development strategies:a semi-automated process utilizing the AI tools and a traditional manual approach.The results demonstrated a significant reduction in total development time,ranging from 40%to 51%.However,it was observed that the generated content could be inaccurate and incomplete,necessitating review and debugging before being applied to projects.In conclusion,given the increasing shift towards automation in software engineering,further research is critical to enhance the efficiency and reliability of AI tools,particularly those that leverage natural language processing(NLP)technologies.展开更多
This paper reports on a pioneer effort for the establishment of a software composite metric with key capability of distinguishing among different structures. As a part of this effort most of the previously proposed pr...This paper reports on a pioneer effort for the establishment of a software composite metric with key capability of distinguishing among different structures. As a part of this effort most of the previously proposed program control-flow complexity metrics are evaluated. It is observed that most of these metrics are inherently limited in distinguishing capability. However, the concept of composite metrics is potentially useful for the development of a practical metrics. This paper presents a methodology for the develop- ment of a practical composite metric using statistical techniques. The proposed metric differs from all previous metrics in 2 ways: (1) It is based on an overall structural analysis of a given program in deeper and broader context. It captures various structural measurements taken from all existing structural levels; (2) It unifies a set of 19 important structural metrics. The compositing model of these metrics is based on statistical techniques rather than on an arbitrary method. Experinces with the pro- posed metric clearly indicate that it distinguishes different structures better than the previous metrics.展开更多
文摘In recent years,automation has become a key focus in software development as organizations seek to improve efficiency and reduce time-to-market.The integration of artificial intelligence(AI)tools,particularly those using natural language processing(NLP)like ChatGPT,has opened new possibilities for automating various stages of the development lifecycle.The primary objective of this study is to evaluate the effectiveness of ChatGPT in automating various phases of software development.An artificial intelligence(AI)tool was developed using the OpenAI—Application Programming Interface(API),incorporating two key functionalities:1)generating user stories based on case or process inputs,and 2)estimating the effort required to execute each user story.Additionally,ChatGPT was employed to generate application code.The AI tool was tested in three case studies,each explored under two different development strategies:a semi-automated process utilizing the AI tools and a traditional manual approach.The results demonstrated a significant reduction in total development time,ranging from 40%to 51%.However,it was observed that the generated content could be inaccurate and incomplete,necessitating review and debugging before being applied to projects.In conclusion,given the increasing shift towards automation in software engineering,further research is critical to enhance the efficiency and reliability of AI tools,particularly those that leverage natural language processing(NLP)technologies.
文摘This paper reports on a pioneer effort for the establishment of a software composite metric with key capability of distinguishing among different structures. As a part of this effort most of the previously proposed program control-flow complexity metrics are evaluated. It is observed that most of these metrics are inherently limited in distinguishing capability. However, the concept of composite metrics is potentially useful for the development of a practical metrics. This paper presents a methodology for the develop- ment of a practical composite metric using statistical techniques. The proposed metric differs from all previous metrics in 2 ways: (1) It is based on an overall structural analysis of a given program in deeper and broader context. It captures various structural measurements taken from all existing structural levels; (2) It unifies a set of 19 important structural metrics. The compositing model of these metrics is based on statistical techniques rather than on an arbitrary method. Experinces with the pro- posed metric clearly indicate that it distinguishes different structures better than the previous metrics.
