An eye-tracking study of 18 developers reading and summarizing Java methods is presented. The developers provide a written summary for methods assigned to them. In total, 63 methods are used from five different systems. Previous studies on this topic use only short methods presented in isolation usually as images. In contrast, this work presents the study in the Eclipse IDE allowing access to all the source code in the system. The developer can navigate via scrolling and switching files while writing the summary. New eye-tracking infrastructure allows for this improvement in the study environment. Data collected includes eye gazes on source code, written summaries, and time to complete each summary. Unlike prior work that concluded developers focus on the signature the most, these results indicate that they tend to focus on the method body more than the signature. Moreover, both experts and novices tend to revisit control flow terms rather than reading them for a long period. They also spend a significant amount of gaze time and have higher gaze visits when they read call terms. Experts tend to revisit the body of the method significantly more frequently than its signature as the size of the method increases. Moreover, experts tend to write their summaries from source code lines that they read the most.
@cite_15 use method stereotypes @cite_26 and class stereotypes @cite_31 to generate natural language summaries for Java classes. @cite_38 @cite_27 use method stereotypes to generate a standard summary for C++ methods via static analysis. McBurney and McMillan propose generating documentation summaries for Java methods using the call graph @cite_45 . Furthermore, they propose an approach to evaluate a summary using textual similarity of that summary to the source code @cite_29 . @cite_16 investigate the suitability of several text summarization techniques to automatically generate term-based summaries for methods and classes. This was further extended by @cite_46 using a new technique named Hierarchical PAM.
@cite_38 @cite_31 @cite_26 @cite_46 @cite_29 @cite_27 @cite_45 @cite_15 @cite_16
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An approach to automatically generate natural language documentation summaries for C++ methods is presented. The approach uses prior work by the authors on stereotyping methods along with the source code analysis framework srcML. First, each method is automatically assigned a stereotype(s) based on static analysis and a set of heuristics. Then, the approach uses the stereotype information, static analysis, and predefined templates to generate a natural-language summary for each method. This summary is automatically added to the code base as a comment for each method. The predefined templates are designed to produce a generic summary for specific method stereotypes. Static analysis is used to extract internal details about the method (e.g., parameters, local variables, calls, etc.). This information is used to specialize the generated summaries. Two studies are conducted to evaluate an approach to automatically generate natural language documentation summaries for C++ methods. The documentation approach relies on a method's stereotype information. First, each method is automatically assigned a stereotype(s) based on static analysis and a set of heuristics. Then, the approach uses the stereotype information, static analysis, and predefined templates to generate a natural-language summary documentation for each method. This documentation is automatically added to the code base as a comment for each method. The result of the first study reveals that the generated documentation is accurate, does not include unnecessary information, and does a reasonable job describing what the method does. Based on statistical analysis of the second study, the most important part of the documentation is the short description as it describes the intended behavior of a method. Most software engineering tasks require developers to understand parts of the source code. When faced with unfamiliar code, developers often rely on (internal or external) documentation to gain an overall understanding of the code and determine whether it is relevant for the current task. Unfortunately, the documentation is often absent or outdated. This paper presents a technique to automatically generate human readable summaries for Java classes, assuming no documentation exists. The summaries allow developers to understand the main goal and structure of the class. The focus of the summaries is on the content and responsibilities of the classes, rather than their relationships with other classes. The summarization tool determines the class and method stereotypes and uses them, in conjunction with heuristics, to select the information to be included in the summaries. Then it generates the summaries using existing lexicalization tools. A group of programmers judged a set of generated summaries for Java classes and determined that they are readable and understandable, they do not include extraneous information, and, in most cases, they are not missing essential information. Text summary helps in understanding the content of a text without having to read the contents of the text as a whole. Automatic text summarization can be used to summarize the text easier. In this paper a frequent term based text summarization for Bahasa Indonesia is designed and implemented in java. The proposed system generates a summary for a given input document based on identification and extraction of important sentences in the document. The system counts nouns and verbs term frequency because they are considered as the most representative to the content of the text. The system also integrated to statistical approach with two underlying concepts such as title of the news article and location of the sentence. The generated summaries were compared with human generated summaries. Precision, recall and f-measure ratio are used to evaluate the accuracy of the generated summary. Assessment of the system summary result quality by respondents is also done by giving a value from 1 to 100. Based on the experimental results, the system is able to produce an effective summary with the average f-measure of 78 , at the compression rate of 30 . The average value of the quality of system summary result provided by respondents is 83,3