A Lightweight Approach of Human-Like Playtest for Android Apps

Testing is recognized as a key and challenging factor that can either boost or halt the game development in the mobile game industry. On one hand, manual testing is expensive and time-consuming, especially the wide spectrum of device hardware and software, so called fragmentation, significantly increases the cost to test applications on devices manually. On the other hand, automated testing is also very difficult due to more inherent technical issues to test games as compared to other mobile applications, such as non-native widgets, nondeterminism , complex game strategies and so on. Current testing frameworks (e.g., Android Monkey, Record and Replay) are limited because they adopt no domain knowledge to test games. Learning-based tools (e.g., Wuji) require tremendous resources and manual efforts to train a model before testing any game. The high cost of manual testing and lack of efficient testing tools for mobile games motivated the work presented in this thesis which aims to explore easy and efficient approaches to test mobile games efficiently and effectively. A new Android mobile game testing tool, called LIT, has been developed. LIT is a lightweight approach to generalize playtest tactics from manual testing, and to adopt the tactics for automatic game testing. LIT has two phases: tactic generalization and tactic concretization. In Phase I, when a human tester plays an Android game G for awhile (e.g., eight minutes), LIT records the tester's inputs and related scenes. Based on the collected data, LIT infers a set of context-aware, abstract playtest tactics that describe under what circumstances, what actions can be taken. In Phase II,LIT tests G based on the generalized tactics. Namely, given a randomly generated game scene, LIT tentatively matches that scene with the abstract context of any inferred tactic; if the match succeeds, LIT customizes the tactic to generate an action for playtest. Our evaluation with nine games shows LIT to outperform two state-of-the-art tools and are reinforcement learning (RL)-based tool, by covering more code and triggering more errors. This implies that LIT complements existing tools and helps developers better test certain games (e.g., match3).

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2022

Author dc:creator

• Zhao, Yan

Chair dc:contributor.committeechair

• Meng, Na

Committee members dc:contributor.committeemember

• Tilevich, Eli
• Cai, Haipeng

• automated game testing
• playtest
• tactic generalization
• tactic concretization

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:33785

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/108077