Identifying gameplay videos that exhibit bugs in computer games

Lin, Dayi; Bezemer, Cor‐Paul; Hassan, Ahmed E.

doi:10.1007/s10664-019-09733-6

Cited by 19 publications

(39 citation statements)

References 25 publications

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“…Yu et al [317] initially focused on defect prediction for concurrent programs and proposed ConPredictor, a prototype tool that used four classiication techniques to identify defects by applying a set of static and dynamic code metrics based on unique features of concurrent programs. Lin et al [142] present an approach to identifying buggy videos from a set of gameplay videos. They applied three classiication models ś logistic regression, neural network and random forests ś to determine the probability that a video showcases a bug.…”

Section: Sotware Maintenancementioning

confidence: 99%

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Predictive Models in Software Engineering: Challenges and Opportunities

Yang

Xia

et al. 2022

ACM Trans. Softw. Eng. Methodol.

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Predictive models are one of the most important techniques that are widely applied in many areas of software engineering. There have been a large number of primary studies that apply predictive models and that present well-performed studies in various research domains, including software requirements, software design and development, testing and debugging and software maintenance. This paper is a first attempt to systematically organize knowledge in this area by surveying a body of 421 papers on predictive models published between 2009 and 2020. We describe the key models and approaches used, classify the different models, summarize the range of key application areas, and analyze research results. Based on our findings, we also propose a set of current challenges that still need to be addressed in future work and provide a proposed research road map for these opportunities.

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Section: Sotware Maintenancementioning

confidence: 99%

“…Key reasons are usually to save developer time and efort and to improve the software quality in terms of stability, reliability, and security. Many of such studies have resulted in great improvements in various tasks [24,142,233,240,267,326].…”

Section: Introductionmentioning

confidence: 99%

Predictive Models in Software Engineering: Challenges and Opportunities

Yang

Xia

et al. 2022

ACM Trans. Softw. Eng. Methodol.

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“…To statistically compare the interpretation consistency in different performance groups, we perform the Wilcoxon Rank Sum (WRS) test. The Wilcoxon Rank Sum test is an unpaired, non- parametric test commonly used in literature [15,45,46], of which the null hypothesis is that for randomly selected values X and Y from two distributions, the probability of X being greater than Y is equal to the probability of Y being greater than X. In this RQ, to confirm if the interpretation consistency of group X is statistically significantly higher than group Y, we use a one-sided alternative hypothesis of X being shifted to the right of Y.…”

Section: Approachmentioning

confidence: 99%

Towards a Consistent Interpretation of AIOps Models

LyuYingzhe

Rajbahadur

Lin

et al. 2021

ACM Trans. Softw. Eng. Methodol.

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Artificial Intelligence for IT Operations (AIOps) has been adopted in organizations in various tasks, including interpreting models to identify indicators of service failures. To avoid misleading practitioners, AIOps model interpretations should be consistent (i.e., different AIOps models on the same task agree with one another on feature importance). However, many AIOps studies violate established practices in the machine learning community when deriving interpretations, such as interpreting models with suboptimal performance, though the impact of such violations on the interpretation consistency has not been studied. In this article, we investigate the consistency of AIOps model interpretation along three dimensions: internal consistency, external consistency, and time consistency. We conduct a case study on two AIOps tasks: predicting Google cluster job failures and Backblaze hard drive failures. We find that the randomness from learners, hyperparameter tuning, and data sampling should be controlled to generate consistent interpretations. AIOps models with AUCs greater than 0.75 yield more consistent interpretation compared to low-performing models. Finally, AIOps models that are constructed with the Sliding Window or Full History approaches have the most consistent interpretation with the trends presented in the entire datasets. Our study provides valuable guidelines for practitioners to derive consistent AIOps model interpretation.

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“…Random Forest is an ensemble classification-based algorithm that contains a set of decision trees. These decision trees give classifications and are created from data samples that decide which classification has the most votes [70]. Then, the algorithm selects the trees that give the best prediction result.…”

Section: ) Random Forest Classifiermentioning

confidence: 99%

CaPBug-A Framework for Automatic Bug Categorization and Prioritization Using NLP and Machine Learning Algorithms

2021

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Bug reports facilitate software development teams in improving the quality of software. These reports include significant information related to problems encountered within a software, possible enhancement suggestions, and other potential issues. Bug reports are typically complex and are too detailed; hence a lot of resources are required to analyze and process them manually. Moreover, it leads to delays in the resolution of high priority bugs. Accurate and timely processing of bug reports based on their category and priority plays a significant role in improving the quality of software maintenance. Therefore, an automated process of categorization and prioritization of bug reports is needed to address the aforementioned issues. Automated categorization and prioritization of bug reports have been explored recently by many researchers; however, limited progress has been made in this regard. In this research, we present a novel framework, titled CaPBug, for automated categorization and prioritization of bug reports. The framework is implemented using Natural Language Processing (NLP) and supervised machine learning algorithms. A baseline corpus is built with six categories and five prioritization levels by analyzing more than 2000 bug reports of Mozilla and Eclipse repository. Four classification algorithms i.e., Naive Bayes, Random Forest, Decision Tree, and Logistic Regression have been used to categorize and prioritize bug reports. We demonstrate that the CaPBug framework achieved an accuracy of 88.78% by using a Random Forest classifier with a textual feature for predicting the category. Similarly, using the CaPBug framework, an accuracy of 90.43% was achieved in predicting the priority of bug reports. Synthetic Minority Over-Sampling Technique (SMOTE) has been applied to address the class imbalance issue in priority classes.

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Identifying gameplay videos that exhibit bugs in computer games

Cited by 19 publications

References 25 publications

Predictive Models in Software Engineering: Challenges and Opportunities

Predictive Models in Software Engineering: Challenges and Opportunities

Towards a Consistent Interpretation of AIOps Models

CaPBug-A Framework for Automatic Bug Categorization and Prioritization Using NLP and Machine Learning Algorithms

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