Research On The Method Of Generating Composite Solution For Computer Program Development

With the development and maturity of computer technology,new software development tools and technologies are constantly emerging,and the old software development technologies are constantly changing new versions to adapt to the new development needs.At the same time,the problem is that the means to solve the problem become diversified,and the difficulty to solve the problem becomes more and more.At present,the update and improvement speed of software technical description document cannot cover all problems,so most computer program developers use network community to raise their own problems,waiting for other users to solve them.With the development of Internet community,there are more and more valuable information in it.Nowadays,most of the computer program developers begin to retrieve the problems they encounter from the existing network community data.However,because the users of the open community come from all over the world and have different ways of expression,the search effect is not good,and they cannot make full use of the existing massive valuable data to solve the problem.In order to solve the above problems,this paper proposes a method to generate a compound solution of computer program development problems.This method uses the technology of word vector and subject word extraction to express the semantics of the text segment to the maximum extent in the way of vector quantization.It analyzes the semantics and compares the similarity between the new problem and all the existing problem texts,and starts from the massive computer programs The best answer to solve the new problem can be found in the question and answer data text of the open question and answer community.According to the analysis of the extracted answer text and some official document paragraphs,we can provide the support of the closest official document for the extracted optimal answer,synthesize the above two parts of conclusion text to generate the final composite solution,and at the same time,the official document paragraphs make up for the poor readability of the answer caused by the inconsistency of the technical level of the answer producers and readers.Aiming at the poor quality of the open Q & a community text,this paper proposes an automatic spelling correction algorithm for massive low-quality text data,which improves the quality of the text corpus to a certain extent,and improves the accuracy of the subsequent calculation.This paper uses the question and answer text data of stack overflow,which is an active online question and answer community.Compared with the traditional text retrieval algorithm based on word index,the compound solution generation method proposed in this paper can generate better answers.At the same time,the introduction of official document segments also improves the ability of the solution generated in this paper.