1,388 research outputs found
Automatic bug triaging techniques using machine learning and stack traces
When a software system crashes, users have the option to report the crash using automated bug tracking systems. These tools capture software crash and failure data (e.g., stack traces, memory dumps, etc.) from end-users. These data are sent in the form of bug (crash) reports to the software development teams to uncover the causes of the crash and provide adequate fixes. The reports are first assessed (usually in a semi-automatic way) by a group of software analysts, known as triagers. Triagers assign priority to the bugs and redirect them to the software development teams in order to provide fixes.
The triaging process, however, is usually very challenging. The problem is that many of these reports are caused by similar faults. Studies have shown that one way to improve the bug triaging process is to detect automatically duplicate (or similar) reports. This way, triagers would not need to spend time on reports caused by faults that have already been handled. Another issue is related to the prioritization of bug reports. Triagers often rely on the information provided by the customers (the report submitters) to prioritize bug reports. However, this task can be quite tedious and requires tool support. Next, triagers route the bug report to the responsible development team based on the subsystem, which caused the crash. Since having knowledge of all the subsystems of an ever-evolving industrial system is impractical, having a tool to automatically identify defective subsystems can significantly reduce the manual bug triaging effort.
The main goal of this research is to investigate techniques and tools to help triagers process bug reports. We start by studying the effect of the presence of stack traces in analyzing bug reports. Next, we present a framework to help triagers in each step of the bug triaging process. We propose a new and scalable method to automatically detect duplicate bug reports using stack traces and bug report categorical features. We then propose a novel approach for predicting bug severity using stack traces and categorical features, and finally, we discuss a new method for predicting faulty product and component fields of bug reports.
We evaluate the effectiveness of our techniques using bug reports from two large open-source systems. Our results show that stack traces and machine learning methods can be used to automate the bug triaging process, and hence increase the productivity of bug triagers, while reducing costs and efforts associated with manual triaging of bug reports
Duplicate bug report detection using clustering
Bug reporting and fixing the reported bugs play a critical part in the development and maintenance of software systems. The software developers and end users can collaborate in this process to improve the reliability of software systems. Various end users report the defects they have found in the software and how these bugs affect them. However, the same defect may be reported independently by several users leading to a significant number of duplicate bug reports. There are a number of existing methods for detecting duplicate bug reports, but the best results so far account for only 24% of actual duplicates. In this paper, we propose a new method based on clustering to identify a larger proportion of duplicate bug reports while keeping the false positives of misidentified non-duplicates low. The proposed approach is experimentally evaluated on a large sample of bug reports from three public domain data sets. The results show that this approach achieves better performance in terms of a harmonic measure that combines true positive and true negative rates when compared to the existing methods
A Comparative Study of Text Embedding Models for Semantic Text Similarity in Bug Reports
Bug reports are an essential aspect of software development, and it is
crucial to identify and resolve them quickly to ensure the consistent
functioning of software systems. Retrieving similar bug reports from an
existing database can help reduce the time and effort required to resolve bugs.
In this paper, we compared the effectiveness of semantic textual similarity
methods for retrieving similar bug reports based on a similarity score. We
explored several embedding models such as TF-IDF (Baseline), FastText, Gensim,
BERT, and ADA. We used the Software Defects Data containing bug reports for
various software projects to evaluate the performance of these models. Our
experimental results showed that BERT generally outperformed the rest of the
models regarding recall, followed by ADA, Gensim, FastText, and TFIDF. Our
study provides insights into the effectiveness of different embedding methods
for retrieving similar bug reports and highlights the impact of selecting the
appropriate one for this task. Our code is available on GitHub.Comment: 7 Page
A Replicated Study on Duplicate Detection: Using Apache Lucene to Search Among Android Defects
Context: Duplicate detection is a fundamental part of issue management. Systems able to predict whether a new defect report will be closed as a duplicate, may decrease costs by limiting rework and collecting related pieces of information. Goal: Our work explores using Apache Lucene for large-scale duplicate detection based on textual content. Also, we evaluate the previous claim that results are improved if the title is weighted as more important than the description. Method: We conduct a conceptual replication of a well-cited study conducted at Sony Ericsson, using Lucene for searching in the public Android defect repository. In line with the original study, we explore how varying the weighting of the title and the description affects the accuracy. Results: We show that Lucene obtains the best results when the defect report title is weighted three times higher than the description, a bigger difference than has been previously acknowledged. Conclusions: Our work shows the potential of using Lucene as a scalable solution for duplicate detection
Cupid: Leveraging ChatGPT for More Accurate Duplicate Bug Report Detection
Duplicate bug report detection (DBRD) is a long-standing challenge in both
academia and industry. Over the past decades, researchers have proposed various
approaches to detect duplicate bug reports more accurately. With the recent
advancement of deep learning, researchers have also proposed several approaches
that leverage deep learning models to detect duplicate bug reports. A recent
benchmarking study on DBRD also reveals that the performance of deep
learning-based approaches is not always better than the traditional approaches.
However, traditional approaches have limitations, e.g., they are usually based
on the bag-of-words model, which cannot capture the semantics of bug reports.
To address these aforementioned challenges, we seek to leverage
state-of-the-art large language model to improve the performance of the
traditional DBRD approach.
In this paper, we propose an approach called Cupid, which combines the
best-performing traditional DBRD approach REP with the state-of-the-art large
language model ChatGPT. Specifically, we first leverage ChatGPT under the
zero-shot setting to get essential information on bug reports. We then use the
essential information as the input of REP to detect duplicate bug reports. We
conducted an evaluation on comparing Cupid with three existing approaches on
three datasets. The experimental results show that Cupid achieves new
state-of-the-art results, reaching Recall Rate@10 scores ranging from 0.59 to
0.67 across all the datasets analyzed. Our work highlights the potential of
combining large language models to improve the performance of software
engineering tasks.Comment: Work in progres
Implementation of Anomaly Detection Using Data Mining Technique
The Purpose of data mining is extracting vital information from huge databases or the data warehouses. Many Data mining applications have used for commercial & scientific sides. This type of study emphatically discusses Data Mining applications into scientific side. Here Scientific data mining differentiates itself and explores that nature of datasets is various from present market concentrated data mining applications. Most people use pattern matching in some form. Search engines on Web use pattern matching to locate information of interest
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