A large-scale empirical study of just-in-time quality assurance

Kamei, Yasutaka; Shihab, Emad; Adams, Bram; Hassan, Ahmed E.; Mockus, Audris; Sinha, Anand; Ubayashi, Naoyasu

doi:10.1109/tse.2012.70

Cited by 569 publications

(771 citation statements)

References 61 publications

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“…In recent years, several research studies propose just-intime defect prediction techniques that are able to predict defective changes (i.e., commits to a version control system) [1], [7]. Just-in-time defect prediction is more practical because it can not only ensure software quality in the development process, but also make the developers check and fix the defects just at the time they are introduced.…”

Section: Introductionmentioning

confidence: 99%

“…In a recent work, Kamei et al perform a large-scale empirical study on just-in-time defect prediction, and propose the usage of the logistic regression algorithm to build a prediction model [1].…”

Section: Introductionmentioning

confidence: 99%

“…To evaluate Deeper, we use two widely used metrics which were also used to evaluate the approach proposed by Kamei et al [1]: cost effectiveness [12], [13], [14], [15], and F1-score [7], [12], [16], [17]. Cost effectiveness evaluates prediction performance considering a given cost threshold, e.g., a certain percentage of code to inspect.…”

Section: Introductionmentioning

confidence: 99%

“…To evaluate the performance of our approach, we use datasets from six large open source projects, i.e., Bugzilla, Columba, JDT, Platform, Mozilla, and PostgreSQL, containing a total of 137,417 changes. We compare our approach with the approach proposed by Kamei et al [1]. The experimental results show that on average across the 6 projects, Deeper could discover 32.22% more bugs than Kamei et al's approach (51.04% versus 18.82% on average).…”

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confidence: 96%

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Deep Learning for Just-in-Time Defect Prediction

Yang

Xia

et al. 2015

2015 IEEE International Conference on Software Quality, Reliability and Security

295

165

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Abstract-Defect prediction is a very meaningful topic, particularly at change-level. Change-level defect prediction, which is also referred as just-in-time defect prediction, could not only ensure software quality in the development process, but also make the developers check and fix the defects in time. Nowadays, deep learning is a hot topic in the machine learning literature. Whether deep learning can be used to improve the performance of justin-time defect prediction is still uninvestigated.In this paper, to bridge this research gap, we propose an approach Deeper which leverages deep learning techniques to predict defect-prone changes. We first build a set of expressive features from a set of initial change features by leveraging a deep belief network algorithm. Next, a machine learning classifier is built on the selected features. To evaluate the performance of our approach, we use datasets from six large open source projects, i.e., Bugzilla, Columba, JDT, Platform, Mozilla, and PostgreSQL, containing a total of 137,417 changes. We compare our approach with the approach proposed by Kamei et al. [1]. The experimental results show that on average across the 6 projects, Deeper could discover 32.22% more bugs than Kamei et al's approach (51.04% versus 18.82% on average). In addition, Deeper can achieve F1-scores of 0.22-0.63, which are statistically significantly higher than those of Kamei et al.'s approach on 4 out of the 6 projects.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 96%

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Deep Learning for Just-in-Time Defect Prediction

Yang

Xia

et al. 2015

2015 IEEE International Conference on Software Quality, Reliability and Security

295

165

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“…Many software problems are due to the development of low quality software that is characterized by numerous defects. Software experts have suggested that the way to address these types of software problems is to improve software quality through quality assurance methods and one of the most well-known software quality techniques is indeed software inspection [1] [2] [3].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Code Inspection on an Outsourced Software Project in Mauritius

Huzooree¹,

Ramdoo²

2015

IJCA

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Software inspection is a proven methodology that enables the detection and removal of defects in software artifacts and thus contributes towards software quality assurance. It eventually leads to significant budget and time benefits. To be most effective, inspections must be an integral part of the software development life cycle and form part of the development schedule. This study considers code inspection as it is the most frequently used inspection in the software development process. For the implementation part of this research, code inspection is carried out in a selected outsourced project in a company situated in Mauritius, and the results are evaluated based on the inspection process and feedback of people involved in the inspection process. General TermsSoftware Code Inspection.

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Multi‐task deep neural networks for just‐in‐time software defect prediction on mobile apps

Huang

2023

Concurrency and Computation

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SummaryWith the development of smartphones, mobile applications play an irreplaceable role in our daily life, which characteristics often commit code changes to meet new requirements. This characteristic can introduce defects into the software. To provide immediate feedback to developers, previous researchers began to focus on just‐in‐time (JIT) software defect prediction techniques. JIT defect prediction aims to determine whether code commits will introduce defects into the software. It contains two scenarios, within‐project JIT defect prediction and cross‐project JIT defect prediction. Regardless of whether within‐project JIT defect prediction or cross‐project JIT defect prediction all need to have enough labeled data (within‐project JIT defect prediction assumes that have plenty of labeled data from the same project, while cross‐project JIT defect prediction assumes that have sufficient labeled data from source projects). However, in practice, both the source and target projects may only have limited labeled data. We propose the MTL‐DNN method based on multi‐task learning to solve this question. This method contains the data preprocessing layer, input layer, shared layers, task‐specific layers, and output layer. Where the common features of multiple related tasks are learned by sharing layers, and the unique features of each task are learned by the task‐specific layers. For verifying the effectiveness of the MTL‐DNN approach, we evaluate our method on 15 Android mobile apps. The experimental results show that our method significantly outperforms the state‐of‐the‐art single‐task deep learning and classical machine learning methods. This result shows that the MTL‐DNN method can effectively solve the problem of insufficient labeled training data for source and target projects.

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A large-scale empirical study of just-in-time quality assurance

Cited by 569 publications

References 61 publications

Deep Learning for Just-in-Time Defect Prediction

Deep Learning for Just-in-Time Defect Prediction

Evaluation of Code Inspection on an Outsourced Software Project in Mauritius

Multi‐task deep neural networks for just‐in‐time software defect prediction on mobile apps

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