Stack Overflow provides a platform for developers to seek suitable solutions by asking questions and receiving answers on various topics.However,many questions are usually not answered quickly enough.Since the questio...Stack Overflow provides a platform for developers to seek suitable solutions by asking questions and receiving answers on various topics.However,many questions are usually not answered quickly enough.Since the questioners are eager to know the specific time interval at which a question can be answered,it becomes an important task for Stack Overflow to feedback the answer time to the question.To address this issue,we propose a model for predicting the answer time of questions,named Predicting Answer Time(i.e.,PAT model),which consists of two parts:a feature acquisition and fusion model,and a deep neural network model.The framework uses a variety of features mined from questions in Stack Overflow,including the question description,question title,question tags,the creation time of the question,and other temporal features.These features are fused and fed into the deep neural network to predict the answer time of the question.As a case study,post data from Stack Overflow are used to assess the model.We use traditional regression algorithms as the baselines,such as Linear Regression,K-Nearest Neighbors Regression,Support Vector Regression,Multilayer Perceptron Regression,and Random Forest Regression.Experimental results show that the PAT model can predict the answer time of questions more accurately than traditional regression algorithms,and shorten the error of the predicted answer time by nearly 10 hours.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.61902050,61602077 and 61672122the China Postdoctoral Science Foundation under Grant No.2020M670736+1 种基金the Fundamental Research Funds for the Central Universities of China under Grant Nos.3132019355 and 2020cxxmss14the High Education Science and Technology Planning Program of Shandong Provincial Education Department of China under Grant Nos.J18KA340 and J18KA385.
文摘Stack Overflow provides a platform for developers to seek suitable solutions by asking questions and receiving answers on various topics.However,many questions are usually not answered quickly enough.Since the questioners are eager to know the specific time interval at which a question can be answered,it becomes an important task for Stack Overflow to feedback the answer time to the question.To address this issue,we propose a model for predicting the answer time of questions,named Predicting Answer Time(i.e.,PAT model),which consists of two parts:a feature acquisition and fusion model,and a deep neural network model.The framework uses a variety of features mined from questions in Stack Overflow,including the question description,question title,question tags,the creation time of the question,and other temporal features.These features are fused and fed into the deep neural network to predict the answer time of the question.As a case study,post data from Stack Overflow are used to assess the model.We use traditional regression algorithms as the baselines,such as Linear Regression,K-Nearest Neighbors Regression,Support Vector Regression,Multilayer Perceptron Regression,and Random Forest Regression.Experimental results show that the PAT model can predict the answer time of questions more accurately than traditional regression algorithms,and shorten the error of the predicted answer time by nearly 10 hours.
