An empirical study of the characteristics of popular Minecraft mods

Lee, Daniel; Rajbahadur, Gopi Krishnan; Lin, Dayi; Sayagh, Mohammed; Bezemer, Cor‐Paul; Hassan, Ahmed E.

doi:10.1007/s10664-020-09840-9

Cited by 19 publications

(23 citation statements)

References 64 publications

(102 reference statements)

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“…Tantithamthavorn et al (2018) outlines that presence of correlated and redundant features might result in affecting the computed feature importance ranks of a model. Therefore, similar to prior studies (Jiarpakdee et al, 2019;Tantithamthavorn et al, 2018;Rajbahadur et al, 2019Rajbahadur et al, , 2017Lee et al, 2020;da Costa et al, 2018;McIntosh et al, 2016), we perform a correlation and redundancy analysis on the independent features of our dataset to remove correlated features using the implementation provided by Au-toSpearman method of Rnalytica 9 R package (Tantithamthavorn et al, 2018;Lee et al, 2020;Yatish et al, 2019). We choose the AutoSpearman method to remove the correlated and redundant features as the study by Jiarpakdee et al (2019) showed that selecting features with AutoSpearman typically yields better subset of features than other techniques.…”

Section: Pre-processing Featuresmentioning

confidence: 99%

“…Step 5 Fig. 3 Reopened bug prediction model pipeline in prior studies (Jiarpakdee et al, 2019;Lee et al, 2020;Rajbahadur et al, 2021). Autospearman uses a criteria that selects one feature from a group of highest correlated features which shares the least correlation with other features that are not in the group (Jiarpakdee et al, 2018) based on Spearman correlation score.…”

Section: Feature Importancementioning

confidence: 99%

“…For instance, no prior study in reopened bug prediction tuned the hyperparameters of their studied model, which could, in turn, improve the prediction performance of the constructed reopened bug prediction model (Rajbahadur et al, 2019;Tantithamthavorn et al, 2018). No prior study in reopened bug prediction performed correlation and redundancy analysis on the independent features of their dataset to remove correlated features; however, several recent studies have used correlation and redundancy analysis on their dataset prior to training the prediction model (Jiarpakdee et al, 2019;Tantithamthavorn et al, 2018;Rajbahadur et al, 2019Rajbahadur et al, , 2017Lee et al, 2020;da Costa et al, 2018;McIntosh et al, 2016). This motivated us to do a large scale study of the likelihood of reopening a bug using the latest prediction pipeline.…”

Section: Introductionmentioning

confidence: 99%

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Revisiting reopened bugs in open source software systems

Tagra¹,

Zhang²,

Rajbahadur³

et al. 2022

Preprint

Self Cite

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Reopened bugs can degrade the overall quality of a software system since they require unnecessary rework by developers. Moreover, reopened bugs also lead to a loss of trust in the end-users regarding the quality of the software. Thus, predicting bugs that might be reopened could be extremely helpful for software developers to avoid rework. Prior studies on reopened bug prediction focus only on three open source projects (i.e., Apache, Eclipse, and OpenOffice) to generate insights. We observe that one out of the three projects (i.e., Apache) has a data leak issue -the bug status of reopened was included as training data to predict reopened bugs. In addition, prior studies used an outdated prediction model pipeline (i.e., with old techniques for constructing a prediction model) to predict reopened bugs. Therefore, we revisit the reopened bugs study on a large scale dataset consisting of 47 projects tracked by JIRA using the modern techniques such as SMOTE, permutation importance together with 7 different machine learning models. We study the reopened bugs using a mixed methods approach (i.e., both quantitative and qualitative study). We find that: 1) After using an updated reopened bug prediction model pipeline, only 34% projects give an acceptable performance with AUC 0.7. 2) There are four major reasons for a bug getting reopened, that is, technical (i.e., patch/integration issues), documentation, human (i.e., due to incorrect bug assessment), and reasons not shown in the bug reports. 3) In

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Section: Pre-processing Featuresmentioning

confidence: 99%

Section: Feature Importancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Revisiting reopened bugs in open source software systems

Tagra¹,

Zhang²,

Rajbahadur³

et al. 2022

Preprint

Self Cite

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“…Minecraft) stimulating their sense of innovation and increasing their involvement (Grohn, 2017). Other similar practices surfaced to embrace the high expectations of gamers through mod communitiessupported by distribution platforms such as Steam and Epic games - (Lee et al, 2020;Thorhauge & Nielsen, 2021). The latter allow users to create new mods into their favorite games, thus, encouraging their voluntary will to participate in collaborative innovation (Bilińska-Reformat et al, 2020).…”

Section: Value Co-creationmentioning

confidence: 99%

Consumer engagement in value co-creation within virtual video game communities

Afi

Ouiddad

2021

Management &Amp; Marketing. Challenges for the Knowledge Society

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With the emergence of the gaming industry as the most empowering form of entertainment, video game online communities have become a refuge for many users who are looking to fulfil specific needs, leading them to participate in creating their own experiences. Drawing upon the uses and gratifications theory, this research aims to understand the motivations behind consumer engagement in value co-creation within digital game distribution services (exp. Steam), and how it affects loyalty. To the extent of the author’s knowledge, little research has addressed this question within the gaming industry. We conducted an online survey administered to video game platforms users, through which 195 valid answers were collected. Findings suggest that hedonic along with personal and social motivations have positive influence on user’s engagement to co-create value, which in return increases their loyalty behavior.

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“…In order to provide validation to the findings of the qualitative analysis, we conducted quantitative analysis on a set of 2,527 npm packages grouped into highly-selected and not highly-selected packages. Similar to prior work [6,32,55], we estimated the highly-selected packages based on the number of directly dependent packages (i.e., clients packages) within the npm ecosystem. Then, we mined and analyzed the selected packages and collected quantitative data to present the factors studied in our survey.…”

Section: Introductionmentioning

confidence: 99%

What are the characteristics of highly-selected packages? A case study on the npm ecosystem

Mujahid¹,

Shihab²

2022

Preprint

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With the popularity of software ecosystems, the number of open source components (known as packages) has grown rapidly. Identifying high-quality and well-maintained packages from a large pool of packages to depend on is a basic and important problem, as it is beneficial for various applications, such as package recommendation and package search. However, no systematic and comprehensive work focuses on addressing this problem except in online discussions or informal literature and interviews. To fill this gap, in this paper, we conducted a mixed qualitative and quantitative analysis to understand how developers identify and select relevant open source packages. In particular, we started by surveying 118 JavaScript developers from the npm ecosystem to qualitatively understand the factors that make a package to be highly-selected within the npm ecosystem. The survey results showed that JavaScript developers believe that highly-selected packages are well-documented, receive a high number of stars on GitHub, have a large number of downloads, and do not suffer from vulnerabilities. Then, we conducted an experiment to quantitatively validate the developers' perception of the factors that make a highly-selected package. In this analysis, we collected and mined historical data from 2,527 packages divided into highly-selected and not highly-selected packages. For each package in the dataset, we collected quantitative data to present the factors studied in the developers' survey. Next, we used regression analysis to quantitatively investigate which of the studied factors are the most important. Our regression analysis complements our survey results about highly-selected packages. In particular, the results showed that highly-selected packages tend to be correlated by the number of downloads, stars, and how large the package's readme file is.

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An empirical study of the characteristics of popular Minecraft mods

Cited by 19 publications

References 64 publications

Revisiting reopened bugs in open source software systems

Revisiting reopened bugs in open source software systems

Consumer engagement in value co-creation within virtual video game communities

What are the characteristics of highly-selected packages? A case study on the npm ecosystem

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