TY - GEN
T1 - Type conversion sequence recommendation based on semantic web technology
AU - Yu, Haibo
AU - Jia, Xi
AU - Mine, Tsunenori
AU - Zhao, Jianjun
N1 - Funding Information:
ACKNOWLEDGMENT This research was sponsored in part by 973 Program in China (Grant No.2015CB352203), the National Nature Science Foundation of China (Grant No. 61472242, 61572312, and 61572313). This work is performed during Haibo Yu’s visit to Kyushu University.
PY - 2018/12/4
Y1 - 2018/12/4
N2 - As the software systems are becoming more and more complicated, developers have an increasing dependency on code recommendation tools to assist them to fulfill their development tasks. However, the current historical-code-based recommendation methods are directly affected by the quality of the historical codes and the program-environment-information-based recommendation methods cannot provide satisfactory recommendation results for static methods because it is difficult to know all possible static members only using the program context, and even if we know all the static members, we still cannot add all of them to the entry point for search because its large number may cause a space explosion. In this paper, we propose a type conversion sequence recommendation method based on program environment information. Combing with the reachability analysis using semantic Web technology, the proposed method tries to reduce the searching entry points to solve the space explosion problem caused by the recommendation of static methods. We implemented an Eclipse plug-in based on the proposed method and conducted experiments on Tomcat source code. The experimental results showed that the proposed method can not only recommend type conversion sequences with static methods effectively, but also has a higher accuracy for the recommendation of object methods compared with the Eclipse Code Recommenders.
AB - As the software systems are becoming more and more complicated, developers have an increasing dependency on code recommendation tools to assist them to fulfill their development tasks. However, the current historical-code-based recommendation methods are directly affected by the quality of the historical codes and the program-environment-information-based recommendation methods cannot provide satisfactory recommendation results for static methods because it is difficult to know all possible static members only using the program context, and even if we know all the static members, we still cannot add all of them to the entry point for search because its large number may cause a space explosion. In this paper, we propose a type conversion sequence recommendation method based on program environment information. Combing with the reachability analysis using semantic Web technology, the proposed method tries to reduce the searching entry points to solve the space explosion problem caused by the recommendation of static methods. We implemented an Eclipse plug-in based on the proposed method and conducted experiments on Tomcat source code. The experimental results showed that the proposed method can not only recommend type conversion sequences with static methods effectively, but also has a higher accuracy for the recommendation of object methods compared with the Eclipse Code Recommenders.
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U2 - 10.1109/SmartWorld.2018.00076
DO - 10.1109/SmartWorld.2018.00076
M3 - Conference contribution
AN - SCOPUS:85060283927
T3 - Proceedings - 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
SP - 240
EP - 245
BT - Proceedings - 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
A2 - Loulergue, Frederic
A2 - Wang, Guojun
A2 - Bhuiyan, Md Zakirul Alam
A2 - Ma, Xiaoxing
A2 - Li, Peng
A2 - Roveri, Manuel
A2 - Han, Qi
A2 - Chen, Lei
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE SmartWorld, 15th IEEE International Conference on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
Y2 - 7 October 2018 through 11 October 2018
ER -