Revisiting reopened bugs in open source software systems

Tagra, Ankur; Zhang, Haoxiang; Rajbahadur, Gopi Krishnan; Hassan, Ahmed E.

doi:10.1007/s10664-022-10133-6

“… (Step-1) Draw a statistically representative sample of blocks for each day within our data collection period To bring the data size to more manageable levels, we draw a statistically representative sample of mined blocks for each day within a one-month period. To draw the samples we use a setting of a 95% confidence level and ± 5 confidence interval, similarly to several prior studies (Tagra et al 2022 ; Zhang et al 2019 ; Boslaugh and Watters 2008 ). Following, for each day in our time period we calculate the statistically representative sample of blocks, and sample the corresponding amount.…”

Section: Methodsmentioning

confidence: 99%

What makes Ethereum blockchain transactions be processed fast or slow? An empirical study

Pacheco

¹

,

Oliva

²

,

Rajbahadur³

et al. 2023

Empir Software Eng

Self Cite

7

0

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The Ethereum platform allows developers to implement and deploy applications called ÐApps onto the blockchain for public use through the use of smart contracts. To execute code within a smart contract, a paid transaction must be issued towards one of the functions that are exposed in the interface of a contract. However, such a transaction is only processed once one of the miners in the peer-to-peer network selects it, adds it to a block, and appends that block to the blockchain This creates a delay between transaction submission and code execution. It is crucial for ÐApp developers to be able to precisely estimate when transactions will be processed, since this allows them to define and provide a certain Quality of Service (QoS) level (e.g., 95% of the transactions processed within 1 minute). However, the impact that different factors have on these times have not yet been studied. Processing time estimation services are used by ÐApp developers to achieve predefined QoS. Yet, these services offer minimal insights into what factors impact processing times. Considering the vast amount of data that surrounds the Ethereum blockchain, changes in processing times are hard for ÐApp developers to predict, making it difficult to maintain said QoS. In our study, we build random forest models to understand the factors that are associated with transaction processing times. We engineer several features that capture blockchain internal factors, as well as gas pricing behaviors of transaction issuers. By interpreting our models, we conclude that features surrounding gas pricing behaviors are very strongly associated with transaction processing times. Based on our empirical results, we provide ÐApp developers with concrete insights that can help them provide and maintain high levels of QoS.

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“…Because of the negative impact of bug reopening on software quality [89], we examined the reopening rate of ML and non-ML bugs from the studied system. We aim to understand if developers experience higher bug reopening rates when dealing with ML bugs than they do for non-ML bugs.…”

Section: Rq3: Needed Resources For Fixing Bugs In Ml-based Systemsmentioning

confidence: 99%

Bugs in machine learning-based systems: a faultload benchmark

Morovati

¹

,

Nikanjam

²

,

Khomh

³

et al. 2023

Empir Software Eng

8

0

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Rapid growth of applying Machine Learning (ML) in different domains, especially in safety-critical areas, increases the need for reliable ML components, i.e., a software component operating based on ML. Since corrective maintenance, i.e. identifying and resolving systems bugs, is a key task in the software development process to deliver reliable software components, it is necessary to investigate the usage of ML components, from the software maintenance perspective. Understanding the bugs characteristics and maintenance challenges in ML-based systems can help developers of these systems to identify where to focus maintenance and testing efforts, by giving insights into the most error-prone components, most common bugs, etc. In this paper, we investigate the characteristics of bugs in ML-based software systems and the difference between ML and non-ML bugs from the maintenance viewpoint. We extracted 447,948 GitHub repositories that used one of the three most popular ML frameworks, i.e., TensorFlow, Keras, and PyTorch. After multiple filtering steps, we select the top 300 repositories with the highest number of closed issues. We manually investigate the extracted repositories to exclude non-MLbased systems. Our investigation involved a manual inspection of 386 sampled reported issues in the identified ML-based systems to indicate whether they affect ML components or not. Our analysis shows that nearly half of the real

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“…To bring data size to more manageable levels, we draw a statistically representative sample of mined blocks for each day within a one-month period. We use a 95% confidence level and ± 5 confidence interval, similarly to several prior studies [8,42,53]. Following this sampling approach, we obtain a total of 10,865 blocks (362 blocks per day on average).…”

Section: Approachmentioning

confidence: 99%

What makes Ethereum blockchain transactions be processed fast or slow? An empirical study

Pacheco¹,

Oliva²,

Rajbahadur³

et al. 2022

Preprint

Self Cite

0

View full text Add to dashboard Cite

The Ethereum platform allows developers to implement and deploy applications called ÐApps onto the blockchain for public use through the use of smart contracts. To execute code within a smart contract, a paid transaction must be issued towards one of the functions that are exposed in the interface of a contract. However, such a transaction is only processed once one of the miners in the peer-to-peer network selects it, adds it to a block, and appends that block to the blockchain This creates a delay between transaction submission and code execution. It is crucial for ÐApp developers to be able to precisely estimate when transactions will be processed, since this allows them to define and provide a certain Quality of Service (QoS) level (e.g., 95% of the transactions processed within 1 minute). However, the impact that different factors have on these times have not yet been studied. Processing time estimation services are used by ÐApp developers to achieve predefined QoS. Yet, these services offer minimal insights into what factors impact processing times. Considering the vast amount of data that surrounds the Ethereum blockchain, changes in processing times are hard for ÐApp developers to predict, making it difficult to maintain said QoS. In our study, we build random forest models to understand the factors that are associated with transaction processing times. We engineer several features that capture blockchain internal factors, as well as gas pricing behaviors of transaction issuers. By interpreting our models, we conclude that features surrounding gas pricing behaviors are very strongly associated with transaction processing times. Based on our empirical results, we

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Revisiting reopened bugs in open source software systems

Cited by 14 publications

References 71 publications

What makes Ethereum blockchain transactions be processed fast or slow? An empirical study

What makes Ethereum blockchain transactions be processed fast or slow? An empirical study

Bugs in machine learning-based systems: a faultload benchmark

What makes Ethereum blockchain transactions be processed fast or slow? An empirical study

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