A Method of Code Digest Generation Based on Maximum Entropy Model

Abstract

The present invention provides a code summary generation method based on a maximum entropy model, which collects training samples according to a limited sample template; constructs a code element classifier based on a maximum entropy model according to the training samples; inputs the source code to be analyzed into the classifier, and Identify the code elements in it, and obtain the terms contained in each code element; denoise the acquired terms; according to the code element type to which the term belongs, and specify the weight of each term; according to the weight and the number of occurrences, Evaluate the importance of terms; generate code summaries based on the importance evaluation results and user-specified summary constraints, making the obtained code summaries more accurate.

Description

technical field [0001] The invention relates to a code summary generation method based on a maximum entropy model. Background technique [0002] At each stage of the software life cycle, developers need to spend a lot of time reading program code. During this time, developers tend to avoid understanding the entire system, choosing to focus on only a task-relevant piece of code. To achieve this, developers often skim the code (e.g. only read method signatures). When the knowledge acquired through skimming is not enough to understand the code snippet, they have to spend energy to read the specific information of the code (such as the content in the method body). Although the former method is efficient, it is easy to lose effective information in the code, while the latter method is too time-consuming, and the knowledge gained by skimming the code is difficult to share with other developers. [0003] As a common alternative to skimming, developers often also read code summar...

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Problems solved by technology

[0004] The vast majority of existing code summaries are created manually. Not only does it take a lot of energy from developers to create summaries, but also its maintenance cost is very high. Although academia and industry have also proposed some code summaries based on word frequency, but These techniques often only consider the number and frequency of occurrences of different terms, but ignore the position of the word

A large number of studies have shown that the importance of different words in code is closely related to the type of code element (class, method, variable, etc.) to which they belong; for example: relative to the terms appearing in comments, the importance of terms located in class names is often much higher; and, in existing technical solutions, developers cannot specify certain terms that they need to pay attention to or ignore, for example: in some older legacy codes, the comments in the code have may have long lost consistency with the code, and existing technology still treats comments as important as the code, and may extract obsolete words from comments as part of the code summary

The most similar implementation scheme is the code summarization technology based on word frequency proposed by Haiduc and other scholars of Wayne State University in the United States. Its code summary is inaccurate

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