Supporting program comprehension with source code summarization

Haiduc, Sonia; Aponte, Jairo; Marcus, Andrian

doi:10.1145/1810295.1810335

Cited by 230 publications

(139 citation statements)

References 10 publications

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“…Existing techniques to summarize code have mainly focused on summarizing whole methods [17,18] rather than only summarizing the parts relevant for a given task. Similarly, the approach by Rodeghero et al [34] focused on using eye-tracking to summarize whole methods.…”

Section: Richness Of Eye-tracking Data and Gaze Relevancementioning

confidence: 99%

Tracing software developers' eyes and interactions for change tasks

Kevic

Walters

Shaffer

et al. 2015

Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

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What are software developers doing during a change task? While an answer to this question opens countless opportunities to support developers in their work, only little is known about developers' detailed navigation behavior for realistic change tasks. Most empirical studies on developers performing change tasks are limited to very small code snippets or are limited by the granularity or the detail of the data collected for the study. In our research, we try to overcome these limitations by combining user interaction monitoring with very fine granular eye-tracking data that is automatically linked to the underlying source code entities in the IDE.In a study with 12 professional and 10 student developers working on three change tasks from an open source system, we used our approach to investigate the detailed navigation of developers for realistic change tasks. The results of our study show, amongst others, that the eye-tracking data does indeed capture different aspects than user interaction data and that developers focus on only small parts of methods that are often related by data flow. We discuss our findings and their implications for better developer tool support.

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Section: Richness Of Eye-tracking Data and Gaze Relevancementioning

confidence: 99%

Tracing software developers' eyes and interactions for change tasks

Kevic

Walters

Shaffer

et al. 2015

Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

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“…Source code summarization is also related to earlier techniques in natural language summarization. Spärck-Jones [23] surveys numerous text summarization techniques, and broadly categorizes them as either "extractive", by generating summaries from the content inside a document [3,9,11,20,27], or "abstractive", by generating summaries based on external context or related documents [23]. Finally, topic modeling is widely used in software engineering [1,19] as mentioned in Section 1.…”

Section: Related Workmentioning

confidence: 99%

Improving topic model source code summarization

McBurney

Liu

McMillan

et al. 2014

Proceedings of the 22nd International Conference on Program Comprehension

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In this paper, we present an emerging source code summarization technique that uses topic modeling to select keywords and topics as summaries for source code. Our approach organizes the topics in source code into a hierarchy, with more general topics near the top of the hierarchy. In this way, we present the software's highest-level functionality first, before lower-level details. This is an advantage over previous approaches based on topic models, that only present groups of related keywords without a hierarchy. We conducted a preliminary user study that found our approach selects keywords and topics that the participants found to be accurate in a majority of cases.

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“…There have been a number of studies on text mining for software engineering [20], [29]- [33]. The survey here is by no means complete.…”

Section: B Text Mining For Software Engineeringmentioning

confidence: 99%

Automatic Defect Categorization Based on Fault Triggering Conditions

Xia

Lo²,

Wang³

et al. 2014

2014 19th International Conference on Engineering of Complex Computer Systems

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Abstract-Due to the complexity of software systems, defects are inevitable. Understanding the types of defects could help developers to adopt measures in current and future software releases. In practice, developers often categorize defects into various types. One common categorization is based on fault triggers of defects. Fault trigger is a set of conditions which activate a defect (i.e., fault) and propagate the defect into a failure. In general, there are two types of defect based fault triggering conditions, Bohrbug and Mandelbug. Bohrbug refers to a bug which can be easily isolated, and its activation and error propagation is simple. Mandelbug refers to a bug whose activation and/or error propagation is complex (e.g., a time lag between the fault activation and the failure occurrence). With these category labels, developers can better perform post-mortem analysis to identify common characteristic of the defects, and design specific fault-tolerance mechanisms.However, in most software systems, these category labels are often unavailable. To address this problem, in this paper, we propose a text mining solution which categorize defects into fault trigger categories by analyzing the natural-language description of bug reports. A previous study shows that Mandelbug is more complex and needs more time to be fixed. Thus, to better identify Mandelbugs, we propose a novel fUzzy Set based fEature Selection algorithm named USES, which selects the features (i.e., terms) which have high ability to distinguish Mandelbugs from Bohrbugs. USES first caches a set of terms based on their fuzzy affinity scores to Bohrbug or Mandelbug. Next, it iterates many times, and in each iteration, it selects a subset of terms, and builds a classifier on these terms. USES selects the classifier and the terms which could achieve the best performance on a training data. We evaluate our solution on 4 datasets including Linux, Mysql, Apache HTTPD, and AXIS containing a total of 809 bug reports. We show that USES with naive Bayes multinomial achieves the best performance; it achieves Mandelbug F-measure scores of 0.298 -0.615. We also compare USES with other baseline approaches. The results show that USES on average improves Mandelbug F-measure scores of the best performing baseline by 12.3%.

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Supporting program comprehension with source code summarization

Cited by 230 publications

References 10 publications

Tracing software developers' eyes and interactions for change tasks

Tracing software developers' eyes and interactions for change tasks

Improving topic model source code summarization

Automatic Defect Categorization Based on Fault Triggering Conditions

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