On the Use of Hidden Markov Model to Predict the Time to Fix Bugs

Habayeb, Mayy; Murtaza, Syed Shariyar; Miranskyy, Andriy; Bener, Ayşe

doi:10.1109/tse.2017.2757480

Cited by 43 publications

(94 citation statements)

References 31 publications

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“…We observed that some bugs were rapidly closed in a few seconds just after they were reported to the system, and others had remained with no activity for more than 100 days since there were open. These outlier data patterns on bug reports were also mentioned in the previous research [22]. Some might be interested in seizing characteristics of such abnormality, but we rather focus on modeling with normal data, similarly to [22], [44].…”

Section: A Datasetsmentioning

confidence: 66%

“…Recently, Habayeb et al [22] exploited a hidden Markov model (HMM) to analyze temporal features of systembug repositories, based on their sophisticated feature selections on significant events related to bug condition changes. Similar to this HMM-based approach, we concentrate on the time-dependent features of bug-related activities.…”

Section: Related Workmentioning

confidence: 99%

“…In the same vein of previous works [18]- [22], we consider day-granularity for bug-fix times. Thus, we devise a per-day bin container (namely, activity bin).…”

Section: A Activity Log Streamsmentioning

confidence: 99%

“…where K is the same as the number of the task classes (e.g., K -class classification). To calculate the predicted probability of jth element (y j ) in y task , we use the softmax function (in (22)). Subsequently, for top-k accuracy-based prediction, we choose a k-size set of those elements with the highest probabilities.…”

Section: A Task Learning Processmentioning

confidence: 99%

“…estimation of when a bug (or an issue) will be resolved, if sufficiently accurate, contributes to productive decision-making for resource allocation, release schedule management, and other management tasks. Several researchers investigated the problem of predicting bug-fix times, and most of this research addressed the problem by performing either regression [17], [23]- [25] or classification [18]- [22] based on the features extracted from the attributes of bug reports.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Continual Prediction of Bug-Fix Time Using Deep Learning-Based Activity Stream Embedding

Lee

et al. 2020

IEEE Access

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Predicting the fix time of a bug is important for managing the resources and release milestones of a software development project. However, it is considered non-trivial to achieve high accuracy when predicting bug-fix times. We view that such difficulties come from the lack of continuous or posterior estimation based on subsequent developers' activities after a bug is initially reported. In this paper, we formulate the problem of bug-fix time prediction into a continual update of estimates with more activities. Logging data of bug-related activities that are streamed to a bug tracking system change the bug reports, enabling us to recalculate predictions over time. To do so, we propose a deep learning-based two-staged activity stream embedding model, DASENet that employs (i) a merged network for extracting contextual features across different types of logs, and (ii) a sequence network for exploring temporal relations of the logs. Through experiments with bug tracking system datasets from open source projects including Firefox, Chromium, and Eclipse, we show that DASENet achieves stable performance, e.g., for the Firefox dataset, top-1 accuracy of 4.6 to 8.5 % higher than other state-of-the-art works. Our approach also provides a transferable structure, yielding robust performance with a small dataset for different tasks; the DASENet model trained with a small dataset of about 900 samples (2 % of a full dataset) can show competitive performance to the other models with a full dataset. To the best of our knowledge, we are the first to employ deep learning on log streams in the context of bug-fix time prediction. INDEX TERMS Bug-fix time, activity stream, bug tracking system, deep learning, activity embedding, sequence learning model.

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Section: A Datasetsmentioning

confidence: 66%

Section: Related Workmentioning

confidence: 99%

“…In the same vein of previous works [18]- [22], we consider day-granularity for bug-fix times. Thus, we devise a per-day bin container (namely, activity bin).…”

Section: A Activity Log Streamsmentioning

confidence: 99%

Section: A Task Learning Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Continual Prediction of Bug-Fix Time Using Deep Learning-Based Activity Stream Embedding

Lee

et al. 2020

IEEE Access

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Prediction of bug‐fixing time based on distinguishable sequences fusion in open source software

Ren

et al. 2022

J Software Evolu Process

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Generally, open source software (OSS) has a longer bug‐fixing time. If the bug‐fixing time can be predicted accurately as early as possible, it will be beneficial to the efficiency of bug fixing. Traditional bug‐fixing time prediction models are usually based on static features of bug report. It is difficult to go into service due to inappropriate feature extraction of data and low prediction accuracy of models. The HMM prediction model can predict the bug‐fixing time accurately according to earlier fixing activities. However, this method of temporal sequence feature selection results in a large number of inconsistent samples, and the HMM prediction model can only capture the adjacent activity behavior information of one sequence, and hence, it will reduce the performance of bug‐fixing time prediction. By incorporating the activity information and time information of bug activity transfer, the proportion of inconsistent samples is reduced significantly. In this paper, a double‐sequence input LSTM model (LSTM‐DA) is designed to capture both sequences interaction features and long‐distance‐dependent features. The results of the experiments show that the proposed model can improve the F‐measure and accuracy indicators by about 10% compared with the HMM model in all dimensions, which demonstrates the effectiveness of our method.

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On the value of instance selection for bug resolution prediction performance

Miloudi,

Cheikhi,

Idri

et al. 2024

J Software Evolu Process

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Software maintenance is a challenging and laborious software management activity, especially for open‐source software. The bugs reports of such software allow tracking maintenance activities and were used in several empirical studies to better predict the bug resolution effort. These reports are known for their large size and contain nonrelevant instances that need to be preprocessed to be suitable for use. To this end, instance selection (IS) has been proposed in the literature as a way to reduce the size of the datasets, while keeping the relevant instances. The objective of this study is to perform an empirical study that investigates the impact of data preprocessing through IS on the performance of bug resolution prediction classifiers. To deal with this, four IS algorithms, namely, edited nearest neighbor (ENN), repeated ENN, all‐k nearest neighbors, and model class selection, are applied on five large datasets, together with five machine learning techniques. Overall, 125 experiments were performed and compared. The findings of this study highlight the positive impact of IS in providing better estimates for bug resolution prediction classifiers, in particular using repeated ENN and ENN algorithms.

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On the Use of Hidden Markov Model to Predict the Time to Fix Bugs

Cited by 43 publications

References 31 publications

Continual Prediction of Bug-Fix Time Using Deep Learning-Based Activity Stream Embedding

Continual Prediction of Bug-Fix Time Using Deep Learning-Based Activity Stream Embedding

Prediction of bug‐fixing time based on distinguishable sequences fusion in open source software

On the value of instance selection for bug resolution prediction performance

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