Detecting missing information in bug descriptions

Chaparro, Oscar; Lü, Jing; Zampetti, Fiorella; Moreno, Laura; Penta, Massimiliano Di; Marcus, Andrian; Bavota, Gabriele; Ng, Vincent

doi:10.1145/3106237.3106285

Cited by 116 publications

(55 citation statements)

References 50 publications

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“…However, reporters, especially novices and end users, sometimes find it difficult to do so, because they might not know which features will help developers to fix the bugs . An automated technique that recommends which features to include in bug reports can reduce the number of missing features . Thus, we propose a model that predicts which key features reporters should provide in bug reports that help developers to fix the bugs.…”

Section: Methodsmentioning

confidence: 99%

“…One of the main reasons for the lack of unstructured features in bug reports is inadequate tool support. In order to support reporters, researchers have focused on detecting the presence/absence of unstructured features in bug reports . Zimmerman et al revealed 10 unstructured features that are important for developers in general.…”

Section: Introductionmentioning

confidence: 94%

“…Chaparro et al conducted an empirical study to understand the extent to which Observed Behavior, Steps to Reproduce, and Expected Behavior are reported in bug reports and what discourse patterns (ie, rules that capture the syntax and semantics of the text, eg, “To reproduce” and “STR” are the discourse patterns of Steps to Reproduce) reporters use to describe such information. Then, they designed an automated approach to detect the absence or presence of Steps to Reproduce and Expected Behavior in bug descriptions.…”

Section: Introductionmentioning

confidence: 99%

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Identifying and predicting key features to support bug reporting

Karim

Ihara

Choi

et al. 2019

J Software Evolu Process

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Bug reports are the primary means through which developers triage and fix bugs. To achieve this effectively, bug reports need to clearly describe those features that are important for the developers. However, previous studies have found that reporters do not always provide such features. Therefore, we first perform an exploratory study to identify the key features that reporters frequently miss in their initial bug report submissions. Then, we propose an approach that predicts whether reporters should provide certain key features to ensure a good bug report. A case study of the bug reports for Camel, Derby, Wicket, Firefox, and Thunderbird projects shows that Steps to Reproduce, Test Case, Code Example, Stack Trace, and Expected Behavior are the additional features that reporters most often omit from their initial bug report submissions. We also find that these features significantly affect the bug‐fixing process. On the basis of our findings, we build and evaluate classification models using four different text‐classification techniques to predict key features by leveraging historical bug‐fixing knowledge. The evaluation results show that our models can effectively predict the key features. Our comparative study of different text‐classification techniques shows that naïve Bayes multinomial (NBM) outperforms other techniques. Our findings can benefit reporters to improve the contents of bug reports.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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Identifying and predicting key features to support bug reporting

Karim

Ihara

Choi

et al. 2019

J Software Evolu Process

View full text Add to dashboard Cite

show abstract

“…We trained the word embeddings with dimension 200 on 819K bug reports collected from 358 open source projects using the fastText's skip-gram model implementation [25]. We used data from Chaparro et al [27] to train the model, using data from GUI-based systems only. The character embedding layer consists of one convolution layer with kernel size of 3.…”

Section: Euler Implementation and Calibrationmentioning

confidence: 99%

“…Euler leverages neural sequence labeling [35,38] in combination with discourse patterns [27] and dependency parsing [42] to identify S2R sentences and individual S2Rs. Next, it matches the S2Rs to program states and GUI-level application interactions, represented in a graph-based execution model.…”

mentioning

confidence: 99%

Assessing the quality of the steps to reproduce in bug reports

Chaparro

Bernal-Cárdenas

Lü³

et al. 2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

Self Cite

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A major problem with user-written bug reports, indicated by developers and documented by researchers, is the (lack of high) quality of the reported steps to reproduce the bugs. Low-quality steps to reproduce lead to excessive manual effort spent on bug triage and resolution. This paper proposes Euler, an approach that automatically identifies and assesses the quality of the steps to reproduce in a bug report, providing feedback to the reporters, which they can use to improve the bug report. The feedback provided by Euler was assessed by external evaluators and the results indicate that Euler correctly identified 98% of the existing steps to reproduce and 58% of the missing ones, while 73% of its quality annotations are correct. CCS CONCEPTS• Software and its engineering → Maintaining software.

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IM_NRFixer: A hybrid approach to alleviate class‐imbalance problem for predicting the fixability of Non‐Reproducible bugs

Goyal

Sardana

2020

J Software Evolu Process

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Software maintenance is an important phase in the software development life cycle. Software projects maintain bug repositories to gather, organize, and keep track of bug reports. These bug reports are resolved by numerous software developers. Whenever the reported bug does not get resolved by the assigned developer, he marks the resolution of bug report as Non‐Reproducible (NR). When NR bugs are reconsidered, few of them get resolved, and their resolution changes from NR to fix (NRF). The main aim of this paper is to predict these fixable NRF bug reports. A major challenge in predicting NRF bugs from NR bugs is that only a small portion of NR bugs get fixed, i.e., class‐imbalance problem. For example, NRF bugs account for only 8.64%, 4.73 %, 4.56%, and 1.06% in NetBeans, Eclipse, Open Office, and Mozilla Firefox projects respectively. In this paper, we work on improving the classification performance on these imbalanced datasets. We propose IMNRFixer, a novel and hybrid NRF prediction tool. IMNRFixer uses three different techniques to combat class‐imbalance problem: undersampling, oversampling, and ensemble models. We evaluate the performance of IMNRFixer models on four large and open‐source projects of Bugzilla repository. Our results show that IMNRFixer outperforms conventional machine learning techniques. IMNRFixer achieves performance up to 71.7%, 93.1%, 91.7%, and 96.5% while predicting the minority class (NRF) for NetBeans, Eclipse, Open Office, and Mozilla Firefox projects, respectively.

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Detecting missing information in bug descriptions

Cited by 116 publications

References 50 publications

Identifying and predicting key features to support bug reporting

Identifying and predicting key features to support bug reporting

Assessing the quality of the steps to reproduce in bug reports

IM_NRFixer: A hybrid approach to alleviate class‐imbalance problem for predicting the fixability of Non‐Reproducible bugs

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Detecting missing information in bug descriptions

Cited by 116 publications

References 50 publications

Identifying and predicting key features to support bug reporting

Identifying and predicting key features to support bug reporting

Assessing the quality of the steps to reproduce in bug reports

IMNRFixer: A hybrid approach to alleviate class‐imbalance problem for predicting the fixability of Non‐Reproducible bugs

Contact Info

Product

Resources

About

IM_NRFixer: A hybrid approach to alleviate class‐imbalance problem for predicting the fixability of Non‐Reproducible bugs