Pattern mining and reuse method for user behaviors of Android applications

doi:10.11772/j.issn.1001-9081.2021040652

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (7): 2155-2161.DOI: 10.11772/j.issn.1001-9081.2021040652

• Computer software technology • Previous Articles Next Articles

Pattern mining and reuse method for user behaviors of Android applications

Qun MAO, Weiwei WANG(), Feng YOU, Ruilian ZHAO, Zheng LI

College of Information Science and Technology，Beijing University of Chemical Technology，Beijing 100029，China

Received:2021-04-25 Revised:2021-08-03 Accepted:2021-08-06 Online:2022-07-15 Published:2022-07-10
Contact: Weiwei WANG
About author:MAO Qun， born in 1997， M. S. candidate. Her research interest include software testing.
YOU Feng， born in 1963， M. S.， senior engineer. His research interests include software testing， software reliability， Web user behavior analysis.
ZHAO Ruilian， born in 1964， Ph. D.， professor. Her research interests include software testing， software reliability.
LI Zheng， born in 1974， Ph. D.， professor. His research interest include software regression testing.
Supported by:
National Natural Science Foundation of China(62077003)

Android应用的用户行为模式挖掘及复用方法

毛群, 王微微(), 尤枫, 赵瑞莲, 李征

北京化工大学信息科学与技术学院，北京 100029

通讯作者: 王微微
作者简介:毛群（1997—），女，湖南祁阳人，硕士研究生，主要研究方向：软件测试
尤枫（1963—），男，江苏江都人，高级工程师，硕士，CCF会员，主要研究方向：软件测试、软件可靠性、Web用户行为分析
赵瑞莲（1964—），女，山西忻州人，教授，博士，CCF会员，主要研究方向：软件测试、软件可靠性
李征（1974—），男，河北清苑人，教授，博士，CCF会员，主要研究方向：软件回归测试。
基金资助:
国家自然科学基金资助项目(62077003)

Abstract

Abstract:

Software testing is an effective way to ensure the quality of Android applications. Understanding the functions of Android applications is the basis of the Android testing process. It aims to deeply explore the application’s business logic and reveal its functional defects， playing an important role in testing. User behavior patterns can assist testers in understanding an Android application’s functions， thereby improving test efficiency. Based on the idea “similar Android applications share user behavior patterns”， a user behavior pattern mining and reuse method was proposed to reduce the cost of Android application testing and improve the testing efficiency. Specifically， for the Android application under test， the user behavior patterns from a similar Android application were mined. Then， the semantic-based event fuzzy matching strategy was used to search the corresponding events for the application under test， and the Graphical User Interface （GUI） model based optimal path selection strategy was used to generate target event sequences for the application under test， thereby achieving user behavior pattern reuse across similar applications. The experiments were conducted on 32 user behavior patterns of three categories of Android applications. The results show that 87.4% of user behavior patterns can be completely reused on similar Android applications， and the reused user behavior patterns can effectively cover 90.2% of important states in applications under test. Thus， the proposed method provides effective support for the testing of Android applications.

Key words: Android application, Android application testing, user behavior pattern, pattern mining, pattern reuse

摘要：

软件测试是保障Android应用质量的有效手段，而理解Android应用的功能作为Android测试过程的基础，旨在深入探究应用的业务逻辑并发现其功能问题，在测试中尤为重要。用户行为模式可充分辅助测试人员理解Android应用功能，从而提高测试效率。基于“相似Android应用共享用户行为模式”的构想，提出一种Android应用的用户行为模式挖掘及复用方法，以降低Android应用测试成本并提高测试效率。具体而言，针对待测Android应用，从其相似Android应用挖掘出用户行为模式；然后利用基于语义的事件模糊匹配策略为待测应用寻找对应事件，并基于用户图形界面（GUI）模型的最优路径选择策略为待测应用生成目标事件序列，从而实现相似应用间的用户行为模式复用。针对三类应用的32种用户行为模式进行实验，结果表明，87.4%的行为模式可在相似Android应用上被完整复用，并且被复用的行为模式可有效覆盖待测应用中90.2%的重要状态。可见，所提方法为Android应用测试提供了有效支撑。

关键词: Android应用, Android应用测试, 用户行为模式, 模式挖掘, 模式复用

CLC Number:

TP311.5

Qun MAO, Weiwei WANG, Feng YOU, Ruilian ZHAO, Zheng LI. Pattern mining and reuse method for user behaviors of Android applications[J]. Journal of Computer Applications, 2022, 42(7): 2155-2161.

毛群, 王微微, 尤枫, 赵瑞莲, 李征. Android应用的用户行为模式挖掘及复用方法[J]. 《计算机应用》唯一官方网站, 2022, 42(7): 2155-2161.

Figures/Tables 7

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应用类型	应用ID	应用名称	版本	代码行数
购物清单	S1	Shopping List	v1.0.1	7.1k
	S2	Shopping List	v1.2.3	5.2k
	S3	Shopping List	v1.0.8	18.9k
	S4	OI Shopping List	v1.7.0.5	32.3k
笔记	N1	Swiftnotes	v3.1.4	4.8k
	N2	Note Now	v2.8	6.1k
	N3	Notepad	v1.12	2.7k
	N4	N4 — Notepad	v1.06	2.3k
天气	W1	Geometric Weather	v2.608	77.6k
	W2	Forecastie	v1.5	8.5k
	W3	Good Weather	v4.4	9.7k
	W4	World Weather	v1.2.5	19.7k

应用类型	应用ID	应用名称	版本	代码行数
购物清单	S1	Shopping List	v1.0.1	7.1k
	S2	Shopping List	v1.2.3	5.2k
	S3	Shopping List	v1.0.8	18.9k
	S4	OI Shopping List	v1.7.0.5	32.3k
笔记	N1	Swiftnotes	v3.1.4	4.8k
	N2	Note Now	v2.8	6.1k
	N3	Notepad	v1.12	2.7k
	N4	N4 — Notepad	v1.06	2.3k
天气	W1	Geometric Weather	v2.608	77.6k
	W2	Forecastie	v1.5	8.5k
	W3	Good Weather	v4.4	9.7k
	W4	World Weather	v1.2.5	19.7k

已知应用	待测应用	TP		FN		精确率		召回率
已知应用	待测应用	使用	不使用	使用	不使用	使用	不使用	使用	不使用
S1	S2	83.1	80.0	3.1	6.2	88.5	88.1	96.4	92.9
	S3	86.2	78.5	4.6	9.2	94.9	91.1	94.9	89.5
	S4	64.6	50.7	3.1	20.0	66.7	64.7	95.5	71.7
平均值		77.9	69.7	3.6	11.8	83.4	81.3	95.6	84.7
N1	N2	61.5	42.4	3.9	11.5	88.9	68.8	94.1	78.6
	N3	42.3	38.5	7.7	7.7	61.1	66.7	84.6	83.3
	N4	61.5	46.2	7.7	11.5	80.0	63.2	88.9	80.0
平均值		55.1	42.3	6.4	10.3	76.7	66.2	89.2	80.6
W1	W2	53.6	19.1	0.0	9.5	61.6	24.7	100.0	66.8
	W3	57.1	25.0	0.0	0.0	57.1	25.0	100.0	100.0
	W4	60.7	11.9	1.2	21.4	69.9	17.9	98.1	35.7
平均值		57.2	18.7	0.4	10.3	62.9	22.5	99.4	67.5

已知应用	待测应用	TP		FN		精确率		召回率
已知应用	待测应用	使用	不使用	使用	不使用	使用	不使用	使用	不使用
S1	S2	83.1	80.0	3.1	6.2	88.5	88.1	96.4	92.9
	S3	86.2	78.5	4.6	9.2	94.9	91.1	94.9	89.5
	S4	64.6	50.7	3.1	20.0	66.7	64.7	95.5	71.7
平均值		77.9	69.7	3.6	11.8	83.4	81.3	95.6	84.7
N1	N2	61.5	42.4	3.9	11.5	88.9	68.8	94.1	78.6
	N3	42.3	38.5	7.7	7.7	61.1	66.7	84.6	83.3
	N4	61.5	46.2	7.7	11.5	80.0	63.2	88.9	80.0
平均值		55.1	42.3	6.4	10.3	76.7	66.2	89.2	80.6
W1	W2	53.6	19.1	0.0	9.5	61.6	24.7	100.0	66.8
	W3	57.1	25.0	0.0	0.0	57.1	25.0	100.0	100.0
	W4	60.7	11.9	1.2	21.4	69.9	17.9	98.1	35.7
平均值		57.2	18.7	0.4	10.3	62.9	22.5	99.4	67.5

源应用	目标应用	级别1	级别2	级别3
S1	S2	75.0	33.3	44.4
	S3	57.1	45.5	17.6
	S4	100.0	60.0	42.9
N1	N2	100.0	75.0	100.0
	N3	100.0	100.0	100.0
	N4	100.0	100.0	71.4
W1	W2	100.0	100.0	85.7
	W3	80.0	42.9	69.2
	W4	100.0	100.0	60.0
平均值		90.2	73.0	65.7

Pattern mining and reuse method for user behaviors of Android applications

Android应用的用户行为模式挖掘及复用方法

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