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
Qun MAO, Weiwei WANG(), Feng YOU, Ruilian ZHAO, Zheng LI
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.Supported by:
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:软件测试基金资助:
CLC Number:
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.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021040652
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 |
Tab. 1 Android applications used in experiments
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 名称 | 用户 轨迹数 | 用户 轨迹中 事件数 | 用户 行为 模式数 | 用户行为 模式中 事件数 | 典型功能 |
---|---|---|---|---|---|
S1 | 35 | 274 | 13 | 65 | 1)添加列表 2)删除列表 3)添加商品 4)删除商品 5)编辑列表信息 6)编辑商品信息 7)商品排序 |
N1 | 41 | 278 | 5 | 26 | 1)添加笔记 2)删除笔记 3)搜索并查阅笔记 4)编辑并收藏笔记 |
W1 | 35 | 293 | 14 | 84 | 1)搜索新地点 2)设置数据刷新时间间隔 3)设置温度单位 4)设置距离单位 5)设置压力单位 6)设置风速单位 7)查看应用信 8)设置应用主题 |
Tab. 2 Results of user trajectory and user behavior pattern mining
应用 名称 | 用户 轨迹数 | 用户 轨迹中 事件数 | 用户 行为 模式数 | 用户行为 模式中 事件数 | 典型功能 |
---|---|---|---|---|---|
S1 | 35 | 274 | 13 | 65 | 1)添加列表 2)删除列表 3)添加商品 4)删除商品 5)编辑列表信息 6)编辑商品信息 7)商品排序 |
N1 | 41 | 278 | 5 | 26 | 1)添加笔记 2)删除笔记 3)搜索并查阅笔记 4)编辑并收藏笔记 |
W1 | 35 | 293 | 14 | 84 | 1)搜索新地点 2)设置数据刷新时间间隔 3)设置温度单位 4)设置距离单位 5)设置压力单位 6)设置风速单位 7)查看应用信 8)设置应用主题 |
已知应用 | 待测应用 | TP | FN | 精确率 | 召回率 | ||||
---|---|---|---|---|---|---|---|---|---|
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 |
Tab. 3 Event matching results with and without fuzzy strategy
已知应用 | 待测应用 | TP | FN | 精确率 | 召回率 | ||||
---|---|---|---|---|---|---|---|---|---|
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 |
Tab. 4 State coverage rates of target event sequences on three importance levels
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 |
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