Support system for smartphone application development based on analysis of user reviews

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1 1,a) 1 1 / Support system for smartphone application development based on analysis of user reviews Yuichi Sei 1,a) Yasuyuki Tahara 1 Akihiko Ohsuga 1 Abstract: A number of smartphone applications have been developed these days. However, it is difficult to develop smartphone applications without bugs because they are used in various platforms and environments. Moreover, requirements elicitation is also difficult because various persons may use the smartphone applications. In this paper, we propose an algorithm for eliciting requirements and detecting bugs early by analyzing user reviews posted in review site of smartphone applications. 1. Google Play Apple Store 100 [1] Graduate School of Information Systems, The University of Electro-Communications, Chofu, Tokyo , Japan a) sei@is.uec.ac.jp [14] 2 c 2014 Information Processing Society of Japan 1

1 5 1 3 / 2 3 4 5 6 7 2. 2.1 Google Play App Store 1 Google Play Google Play App Store 1 5 5 Review tle User name 2.2 Fig.

2 / Google Play App Store 1 Google Play Google Play App Store Review tle User name 2.2 Fig. 1 Pos ng date (we can get the detailed me by the Android Market API) 1 Ra ng Sample of a review. Review content ID / / ID ID ID ID 4.2 ID 3. [13] [2] Seyff c 2014 Information Processing Society of Japan 2

3 [11,12] Jones [9] Fu Wiscom [6] Wiscom 1 Chen AR-Miner [4] AR- Miner Galvis Carreño [7] [5, 16] / User reviews Text data Time data Text data & Time data 2 Fig. 2 Topic modeling Anomaly detec on Trend analysis System overview. Requirements elicita on Fault detec on Displaying change of ra ng Latent Dirichlet Allocation (LDA) [3] LDA 3 Algorithm 1 Algorithm 1 Extracting representative reviews of each topic Input: R ID, topicnum Output: t i r i 1: map (Key: a pair of word and topic ID, Value: the probability that the word is categorized to the topic) getlda(r, topicnum) 2: Create array maxv alues with size topicnum 3: Craete array repreviews with size topicnum 4: for r R do 5: Create array temps with size topicnum 6: for w getw ords(r) do 7: for i = 1,..., topicnum do 8: temps[i] temps[i] + map.get((w, i)) 9: end for 10: end for 11: for i = 1,..., topicnum do 12: if temps[i]/ ln(length(r)) > maxv alues[i] then 13: maxv alues[i] temps[i]/ ln(length(r)) 14: repreviews[i] r 15: end if 16: end for 17: end for 18: return repreviews map (w, i), v (w, i) v c 2014 Information Processing Society of Japan 3

4 / X λ r P (r, λ) = λr r! e λ (1) λ T N T V = r=n T P (r, λt ) = 1 N T 1 r=0 (λt ) r e λt (2) r! sl V < sl (3) T 2 3 T T 2 t 0 t 1,..., t i,... T (t i t 0 ) + (t i t 0 )/2i V i i T 3 V 3 Algorithm 2 L.get(i) L i sl sl Algorithm 2 Anomaly detection Input: L λ sl Output: True False 1: minv 1 2: t 0 L.get(0) 3: for N t = 2,..., L do 4: t i L.get(N t 1) 5: T (t i t 0 ) + (t i t 0 )/(2(N t 1)) 6: V 1.0 7: for r = 0,..., N t 1 do 8: V V (λt ) r e λt /r! 9: end for 10: if V < sl then 11: return TRUE 12: end if 13: if V > minv then 14: return FALSE 15: else 16: minv V 17: end if 18: end for 19: return FALSE 4.3 Google Play App Store 5 ID ID d i R i 1 d i r.point r SP i = r R i max(r.point 3, 0) NP i = r R i min(r.point 3, 0) (4) SP i d i NP i d i c 2014 Information Processing Society of Japan 4

5 SP i NP i α α r r r Algorithm 3 getw ords(r) r Length(r) r Algorithm 3 Extracting a representative review Input: R ID Output: R ˆr 1: Create variable maxv alue initialized to 0 2: for r 1 R do 3: Create empty set S 4: for r 2 R do 5: if r 2 r 1 then 6: S S getw ords(r 2 ) 7: end if 8: end for 9: Create variable c initialized to 0 10: for w getw ords(r 1 ) do 11: if S contains w then 12: c c : end if 14: end for 15: if c/ ln(length(r 1 )) > maxv alue then 16: maxv alue c/ ln(length(r 1 )) 17: ˆr r 1 18: end if 19: end for Google Play android market api *1 Google Play Java *1 Mecab LDA Collapsed Gibbs Sampling [8] 5.2 / Google Play 50 17,588 1 Topic Top 5 words in each topic 5 Representative review Google Amazon Amazon 1/ / c 2014 Information Processing Society of Japan 5

6 Topic Top 5 words in each topic Representative review 1 : 2 : ( ) 3 : (^_^)v 4 : 5 PC : 6 : 7 : 8 : 9 ( ) O : ( ) 10 : 11 GOOGLE : Google 12 : 13 (**) : 14 : 15 : : 17 : 18 : 19 :? 20 : 1 Table 1 ( ) Result of extracted topics 9/10 9/11 9/12 9/13 9/14 (a) sl = /10 9/11 9/12 9/13 9/14 (b) sl = Fig. 3 Results of anomaly detection λ = 1/ sl (a) (b) sl = (b) 9/11 9/13 1 sl = 0.01 c 2014 Information Processing Society of Japan 6

7 3(a) 9/12 5 sl 9/ /11 9/12 9/13 Amazon Cookpad 4 0 SP 0 NP α = 1 1 Amazon : Amazon (b) /8/25 : ( ) 0 11/2/26 11/9/14 12/4/1 12/10/18 13/5/6 13/11/22 14/6/10 14/12/ /1/8-20 : (a) Amazon 2013/2/5 : 0 11/9/14 12/4/1 12/10/18 13/5/6 13/11/22 14/6/10 14/12/ /2/ Number of posted reviews Fig Fig (b) Cookpad Results of evaluation change hour 5 Distribution of time of review posting. [15, 17] c 2014 Information Processing Society of Japan 7

8 Android OS Android-Device-Compatibility [10] OS 7. / Amazon Cookpad JSPS , , [9] Jones, S., Poulsen, A., Maiden, N. and Zachos, K.: User roles in asynchronous distributed collaborative idea generation, Proc. ACM Conference on Creativity and cognition (C&C), pp (2011). [10] mixi Inc: GitHub, mixi-inc/android-device-compatibility. [11] Seyff, N., Graf, F. and Maiden, N.: Using Mobile RE Tools to Give End-Users Their Own Voice, Proc. IEEE International Requirements Engineering Conference (RE), pp (2010). [12] Seyff, N., Maiden, N., Karlsen, K., Lockerbie, J., Grünbacher, P., Graf, F. and Ncube, C.: Exploring how to use scenarios to discover requirements, Requirements Engineering, Vol. 14, No. 2, pp (2009). [13] Sutcliffe, A. and Sawyer, P.: Requirements elicitation: Towards the unknown unknowns, Proc. IEEE International Requirements Engineering Conference (RE), pp (2013). [14] Vol. 53, No. 4, pp (2012). [15] Vol. 25, No. 1, pp (2010). [16] Android Vol. 111, No. 469, pp (2012). [17]. Vol. 6, No. 1, pp (2013). [1] AppTornado GmbH: Number of available android applications, number-of-android-apps. [2] Bano, M. and Zowghi, D.: A systematic review on the relationship between user involvement and system success, Information and Software Technology, p. In press (2014). [3] Blei, D. M., Ng, A. Y. and Jordan, M. I.: Latent dirichlet allocation, The Journal of Machine Learning Research, Vol. 3, pp (2003). [4] Chen, N., Lin, J., Hoi, S. C. H., Xiao, X. and Zhang, B.: AR-miner: mining informative reviews for developers from mobile app marketplace, Proc. ICSE, pp (2014). [5] Cinque, M.: Enabling on-line dependability assessment of Android smart phones, 2011 IEEE/IFIP 41st International Conference on Dependable Systems and Networks Workshops (DSN-W), IEEE, pp (2011). [6] Fu, B., Lin, J., Li, L., Faloutsos, C., Hong, J. and Sadeh, N.: Why people hate your app: making sense of user feedback in a mobile app store, Proc. ACM KDD, pp (2013). [7] Galvis Carreño, L. V. and Winbladh, K.: Analysis of user comments: an approach for software requirements evolution, Proc. ICSE, pp (2013). [8] Griffiths, T. L. and Steyvers, M.: Finding scientific topics., Proc. National Academy of Sciences of the United States of America, Vol. 101, No. Suppl 1, pp (2004). c 2014 Information Processing Society of Japan 8

jldadmm: A Java package for the LDA and DMM topic models

jldadmm: A Java package for the LDA and DMM topic models Dat Quoc Nguyen School of Computing and Information Systems The University of Melbourne, Australia dqnguyen@unimelb.edu.au Abstract: In this technical