Supporting Developers in Making Successful Apps

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1 19/10/2015 Supporting Developers in Making Successful Apps Federica Sarro University College London

2 Use the information available in mobile app stores to support developers in making successful apps

3 Use the information available in mobile app stores to support developers in making successful apps Requirements Elicitation from App Description Characteristics of Impactful App Releases Challenges Make it relevant Make it reliable

Elicitation from App Description Characteristics of

4 Making it Relevant

5 Making it Relevant Are app owners/developers really interested in these findings? Do they analyse user reviews for the benefit of next releases? Do they monitor the user feedbacks of competing apps? A. Al Subanin, F. Sarro, S. Black, L. Capra, M. Harman

Do they analyse user reviews for the benefit of next releases?

6 Making it Relevant Are app owners/developers really interested in these findings? Do they analyse user reviews for the benefit of next releases? Do they monitor the user feedbacks of competing apps? What else can be helpful? A. Al Subanin, F. Sarro, S. Black, L. Capra, M. Harman

7 Making it Relevant Are app owners/developers really interested in these findings? We are interviewing app developers to discuss their experience with app stores To understand their point of view and make it count in the upcoming exciting research in app stores If you are an app developer interested in the interview, please drop an to us: A. Al Subanin, F. Sarro, S. Black, L. Capra, M. Harman

and make it count in the upcoming exciting research in app stores If you are an app developer interested in the

8 Have You Ever Mined Data From Mobile App Stores? W. Martin, M. Harman, Y. Jia, F. Sarro, Y. Zhang

9 The App Sampling Problem for App Store Analysis by W. Martin, M. Harman, Y. Jia, F. Sarro, Y. Zheng, MSR 15 Effect of enforced sampling bias Sample size or content is outside of our control App Store data availability Memory or framework limitations W. Martin, M. Harman, Y. Jia, F. Sarro, Y. Zhang

Zheng, MSR 15 Effect of enforced sampling bias Sample size or content is

10 The App Sampling Problem for App Store Analysis by W. Martin, M. Harman, Y. Jia, F. Sarro, Y. Zheng, MSR 15 Authors App Store Set of Set of Type of Reviews Apps Dataset Hoon et al. (tech report '13) Apple F Iacob and Harrison (MSR'13) Google Pa Carreño and Winbladh (ICSE'13) Google Pa Khalid (ICSE'13) Apple Pa Fu et al. (KDD'13) Google F Pagano and Maalej (RE'13) Apple Pa Iacob et al. (BCS-HCI'13) Google Pa Chen et al. (ICSE'14) Google Pa Guzman and Maalej (RE'14) Apple & Google Pa W. Martin, M. Harman, Y. Jia, F. Sarro, Y. Zhang

(tech report '13) Apple 8 700 000 17 000 F Iacob and Harrison (MSR'13) Google 137 000 270 Pa Carreño and Winbladh (ICSE'13) Google 327 3 Pa Khalid (ICSE'13)

11 Impactful Mobile App Releases Characteristics of releases that achieve high impact release text length release day rating price new features bug fixes number of ratings download rank W. Martin, F. Sarro, M. Harman

release day rating price new features bug fixes

12 Impactful Mobile App Releases 1,547 releases (Google Play and Windows Phone) 40% (G) and 55% (W) of releases impacted app performance higher prices, day of release and fewer mentions of bug fixing can increase the chance for a release to be impactful higher prices, more descriptive release text and new features can increase the chance for a release to improve rating W. Martin, F. Sarro, M. Harman

can increase the chance for a release to be impactful higher prices, more descriptive release text

13 Feature Lifecycles as They Spread, Migrate, Remain, and Die in App Stores Federica Sarro, Afnan Al Subaihin, Mark Harman, Yue Jia, William Martin, Yuanyuan Zhang University College London UCL App Store Analysis

William Martin, Yuanyuan Zhang University College London UCL App

14 App Store: A New Avenue for Software Requirements Users Feedback

15 App Store: A New Avenue for Software Requirements Users Feedback

16 App Store: A New Avenue for Software Requirements Users Feedback

17 App Store: A New Avenue for Software Requirements Users Technical Feedback Business Developer

18 App Store: A New Avenue for Software Requirements Users Technical Feedback Business Capturing user reactions Identify trends across the app store Developer

19 Migratory Behaviour of App Features Across Product Categories List Event

20 Migratory Behaviour of App Features Across Product Categories List Event Set Theoretic Characterisation Empirical Study

21 What Developers May Ask Which migratory behaviours carry monetary value? Which migratory behaviours involve more popular features? Which categories are more likely to migrate features to one other?

22 What Developers May Ask Which migratory behaviours involve more popular features? Points Of Interest List Events Show Contact Detail Picture

23 Migratory Behaviour of App Features Across Product Categories List Event Set Theoretic Characterisation Empirical Study

24 Set Theoretic Characterisation of App Store Feature Migration Migratory Behaviour Non- Migratory Behaviour The Theoretical Feature Migration Subsumption Hierarchy

25 Snapshots App Database snapshot t0 snapshot t1 snapshot t2 snapshot t3

26 Snapshots App Database snapshot snapshot t1 snapshot snapshot Category 1 F1 Category 2 Category Membership F2 F1 F3 Category 3 F3 F4 F1 F3 is member of is member of { { { {

27 Weak Migration F C1 A feature weakly migrates if it resides in at least one new category at the end of the time period considered snapshot t0

28 Weak Migration F C1 C2 A feature weakly migrates if it resides in at least one new category at the end of the time period considered snapshot t0 snapshot t1

29 Strong Migration F C1 C1 C2 A feature spreads from at least one category to at least one new category and remains in all categories in which it originated snapshot t0 snapshot t1

30 Weak Exodus F C1 C1 C2 C3 A feature disappears from at least one of the categories in which it previously resided, while appearing in at least one new category snapshot t0 snapshot t1

31 Strong Exodus F C1 C3 C2 C4 A feature disappears from all categories in which it previously resided to take up residence in at least one new category snapshot t0 snapshot t1

32 Intransitive F C1 C1 C2 C2 An intransitive feature neither appears in any new categories nor does it disappear from any between the start and the end of the time period considered snapshot t0 snapshot t1

33 Intransitive F C1 C1 C2 C2 An intransitive feature neither appears in any new categories nor does it disappear from any between the start and the end of the time period considered snapshot t0 snapshot t1

34 Weak Extinction F C1 C1 C2 A feature disappears from at least one category in which it resided and does not migrate to any new ones snapshot t0 snapshot t1

35 Weak Extinction F C1 C1 C2 A feature disappears from at least one category in which it resided and does not migrate to any new ones snapshot t0 snapshot t1

36 Strong Extinction F C1 C2 A feature disappears from all categories in which it resided and does not migrate to any new ones snapshot t0 snapshot t1

37 Migratory Behaviour of App Features Across Product Categories List Event Set Theoretic Characterisation Empirical Study

38 Subjects Weeks 3 and 36 in ,324 non-free features (w3) Weeks 5 and 36 in non-free features (w5)

39 Subjects Weeks 3 and 36 in ,324 non-free features (w3) Weeks 5 and 36 in non-free features (w5) No App Sampling Problem Martin et al. MSR 15

40 Extracting Features from Description of Apps Samsung App Store Mark Harman, Yue Jia, Yuanyuan Zhang. App Store Mining and Analysis: MSR for App Stores. MSR 2012:

41 RQ1: Feature Migration How do features distribute over the different migratory behaviours?

42 RQ1: Feature Migration Weak Extinction Strong Migration Intransitive Weak Exodus Weak Extinction Strong Migration Intransitive Weak Exodus

43 RQ1: Feature Migration Features often migrate to a category that has similar characteristics Find Location Maps & Navigation Travel

44 RQ1: Feature Migration Some features have clearly transferable value allowing them to migrate across category boundaries LATEST NEWS News Sport & Recreation

45 RQ1: Feature Migration Intransitive features are features that are crucial to a given category [view, gps, status] Navigation [sort, track] Music/Video

46 Which Feature Shall I Include in My App? Intransitive features > must-have requirements for apps in a given category Early identification of the migratory features > undiscovered requirements

47 Other Research Questions RQ2: Differences in Migratory Behaviours Are there any differences in the price, rating and popularity of features that exhibit different migratory behaviours? RQ3: Correlations among Price, Rating and Popularity Are there any differences in the correlations between price, rating, popularity within each form of migratory behaviour?

48 Other Research Questions RQ2: Differences in Migratory Behaviours Are there any differences in the price, rating and popularity of features that exhibit different migratory behaviours? RQ3: Correlations among Price, Rating and Popularity Are there any differences in the correlations between price, rating, popularity within each form of migratory behaviour?

49 Future Directions Interplay between feature migration and user requests

50 Future Directions Interplay between feature migration and user requests Relationship between migrations of claims and migrations of code

51 Future Directions Interplay between feature migration and user requests Relationship between migrations of claims and migrations of code Feature migration in other kinds of software system (e.g. software product lines)

52 Future Directions Interplay between feature migration and user requests Relationship between migrations of claims and migrations of code Feature migration in other kinds of software system (e.g. software product lines) Other longitudinal studies of app stores over periods of time

54 UCLappA website

Research Note RN/14/10. App Store Analysis: Mining App Stores for Relationships between Customer, Business and Technical Characteristics

Research Note RN/14/10. App Store Analysis: Mining App Stores for Relationships between Customer, Business and Technical Characteristics UCL DEPARTMENT OF COMPUTER SCIENCE Research Note RN/14/10 App Store Analysis: Mining App Stores for Relationships between Customer, Business and Technical Characteristics 5 September, 2014 Anthony Finkelstein,