Causal Impact Analysis for App Releases in Google Play

被引:51
作者
Martin, William [1 ]
Sarro, Federica [1 ]
Harman, Mark [1 ]
机构
[1] UCL, London, England
来源
FSE'16: PROCEEDINGS OF THE 2016 24TH ACM SIGSOFT INTERNATIONAL SYMPOSIUM ON FOUNDATIONS OF SOFTWARE ENGINEERING | 2016年
基金
英国工程与自然科学研究理事会;
关键词
App Store Mining and Analysis; Causal Impact; STATISTICS;
D O I
10.1145/2950290.2950320
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
App developers would like to understand the impact of their own and their competitors' software releases. To address this we introduce Causal Impact Release Analysis for app stores, and our tool, CIRA, that implements this analysis. We mined 38,858 popular Google Play apps, over a period of 12 months. For these apps, we identified 26,339 releases for which there was adequate prior and posterior time series data to facilitate causal impact analysis. We found that 33% of these releases caused a statistically significant change in user ratings. We use our approach to reveal important characteristics that distinguish causal significance in Google Play. To explore the actionability of causal impact analysis, we elicited the opinions of app developers: 56 companies responded, 78% concurred with the causal assessment, of which 33% claimed that their company would consider changing its app release strategy as a result of our findings.
引用
收藏
页码:435 / 446
页数:12
相关论文
共 44 条
  • [1] Adams B, 2015, IEEE SOFTWARE, V32, P41
  • [2] [Anonymous], 2002, TECHNICAL REPORT
  • [3] The Impact of API Change- and Fault-Proneness on the User Ratings of Android Apps
    Bavota, Gabriele
    Linares-Vasquez, Mario
    Bernal-Cardenas, Carlos Eduardo
    Di Penta, Massimiliano
    Oliveto, Rocco
    Poshyvanyk, Denys
    [J]. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2015, 41 (04) : 384 - 407
  • [4] CONTROLLING THE FALSE DISCOVERY RATE - A PRACTICAL AND POWERFUL APPROACH TO MULTIPLE TESTING
    BENJAMINI, Y
    HOCHBERG, Y
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1995, 57 (01) : 289 - 300
  • [5] Latent Dirichlet allocation
    Blei, DM
    Ng, AY
    Jordan, MI
    [J]. JOURNAL OF MACHINE LEARNING RESEARCH, 2003, 3 (4-5) : 993 - 1022
  • [6] INFERRING CAUSAL IMPACT USING BAYESIAN STRUCTURAL TIME-SERIES MODELS
    Brodersen, Kay H.
    Gallusser, Fabian
    Koehler, Jim
    Remy, Nicolas
    Scott, Steven L.
    [J]. ANNALS OF APPLIED STATISTICS, 2015, 9 (01) : 247 - 274
  • [7] Ceccarelli M., 2010, 2010 32nd International Conference on Software Engineering (ICSE), P163, DOI 10.1145/1810295.1810320
  • [8] A COEFFICIENT OF AGREEMENT FOR NOMINAL SCALES
    COHEN, J
    [J]. EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT, 1960, 20 (01) : 37 - 46
  • [9] Comino S., 2015, UPDATES MANAGEMENT M
  • [10] Predicting software defects with causality tests
    Couto, Cesar
    Pires, Pedro
    Valente, Marco Tulio
    Bigonha, Roberto S.
    Anquetil, Nicolas
    [J]. JOURNAL OF SYSTEMS AND SOFTWARE, 2014, 93 : 24 - 41