Understanding the mobile game maintenance process based on the version history data
Yang, Mengyuan (2017)
2017
Tietojenkäsittelytieteiden tutkinto-ohjelma - Degree Programme in Computer Sciences
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Hyväksymispäivämäärä
2017-12-31
Julkaisun pysyvä osoite on
http://urn.fi/URN:NBN:fi:uta-201801171065
http://urn.fi/URN:NBN:fi:uta-201801171065
Tiivistelmä
Nowadays, a vast number of mobile games appear in the 'App Store'. The fierce competition pushes the suppliers updating their games continuously. Therefore, understanding the maintenance process of mobile games could provide useful guidelines to plan and conduct the application maintenance.
This research applies two data mining methods to analyze the mobile application version history data in the game category, which records all the versions of each mobile games. Firstly, the data is classified into different types of maintenance. The proportion of maintenance types illustrates the critical maintenance activities of mobile games. Secondly, data clustering is used to group the mobile games which have similar update trends.
The data from text classification phase implies that most of the game maintenance activities are related to adding new features, correcting bugs, improving performance, and optimizing game size and contents. From the result of data clustering, there are four stages could be found from the data. At the first stage, mobile games start with different updating trends. Most of the games release new versions randomly. The dominate maintenance activities are enhancive maintenance. At the second stage, most of games release new features in a relatively high frequency and volume. The dominate maintenance activities are enhancive maintenance as well. At the third stage, mobile games update keeps a high-frequency manner as well, however, the volume can differ. The proportion of other maintenance types start to increase. At the final stage, the release frequency and volume keep in a stable state or gradually decrease. The proportion of other maintenance types continue growing, especially in corrective maintenance and performance.
This research applies two data mining methods to analyze the mobile application version history data in the game category, which records all the versions of each mobile games. Firstly, the data is classified into different types of maintenance. The proportion of maintenance types illustrates the critical maintenance activities of mobile games. Secondly, data clustering is used to group the mobile games which have similar update trends.
The data from text classification phase implies that most of the game maintenance activities are related to adding new features, correcting bugs, improving performance, and optimizing game size and contents. From the result of data clustering, there are four stages could be found from the data. At the first stage, mobile games start with different updating trends. Most of the games release new versions randomly. The dominate maintenance activities are enhancive maintenance. At the second stage, most of games release new features in a relatively high frequency and volume. The dominate maintenance activities are enhancive maintenance as well. At the third stage, mobile games update keeps a high-frequency manner as well, however, the volume can differ. The proportion of other maintenance types start to increase. At the final stage, the release frequency and volume keep in a stable state or gradually decrease. The proportion of other maintenance types continue growing, especially in corrective maintenance and performance.