The Impact of API Change- And Fault-Proneness on the User Ratings of Android Apps

ABSTRACT

The mobile apps market is one of the fastest growing areas in the information technology. In digging their market share, developers must pay attention to building robust and reliable apps. In fact, users easily get frustrated by repeated failures, crashes, and other bugs; hence, they abandon some apps in favor of their competition. In this paper we investigate how the fault- and change-proneness of APIs used by Android apps relates to their success estimated as the average rating provided by the users to those apps. First, in a study conducted on 5,848 (free) apps, we analyzed how the ratings that an app had received correlated with the fault- and change-proneness of the APIs such app relied upon. After that, we surveyed 45 professional Android developers to assess (i) to what extent developers experienced problems when using APIs, and (ii) how much they felt these problems could be the cause for unfavorable user ratings. The results of our studies indicate that apps having high user ratings use APIs that are less fault- and change-prone than the APIs used by low rated apps. Also, most of the interviewed Android developers observed, in their development experience, a direct relationship between problems experienced with the adopted APIs and the users’ ratings that their apps received.

AIM

The aim of this paper is investigate how the fault- and change-proneness of APIs used by Android apps relates to their success estimated as the average rating provided by the users to those apps

SCOPE

The scope of this paper is analyzed how the ratings that an app had received correlated with the fault- and change-proneness of the APIs such app relied upon.

EXISTING SYSTEM

Stability and fault-proneness in the Android API is a sensitive and timely topic, given the frequent releases and the number of applications that use these APIs. Therefore, the goal of this paper is to provide solid empirical evidence and shed some light on the relationship between the success of apps (in terms of user ratings), and the change- and fault-proneness of the underlying APIs (i.e., Android API and third-party libraries). We designed two case studies. In the first study we analyzed to what extent the APIs fault- and change-proneness affect the user ratings of the Android apps using them, while in the second we investigated to what extent Android developers experience problems when using APIs and how much they feel these problems can be causes of unfavorable user ratings/comments

DISADVANTAGES

·      Users easily get frustrated by repeated failures, crashes, and other bugs

·      They abandon some apps in favor of their competition.

PROPOSED SYSTEM

In this project, the purpose of our study is to investigate whether the change- and fault-proneness of APIs used by the app relates (or not) to the app success, measured by its ratings. That is, a heavy usage of fault-prone APIs can lead to repeated failures or even crashes of the apps, hence encouraging users to give low ratings and possibly even abandoning the apps. Similarly, the use of unstable APIs that undergo numerous changes in their interfaces can cause backward compatibility problems or require frequent updates to the apps using those APIs. Such updates, in turn, can introduce defects into the applications using unstable APIs. Results of our first study demonstrate that Android apps having higher user ratings generally use APIs that are less fault- and change-prone than APIs used by low rated apps.

ADVANTAGES

·      APIs used by apps having high user ratings are significantly less fault-prone than APIs used by low rated apps

·      Our findings highlight the importance of avoiding change-and fault-prone APIs, it must be clear that selecting the best APIs to use is far from trivial

SYSTEM CONFIGURATION

HARDWARE REQUIREMENTS:-

·                 Processor   -   Pentium –III

·                Speed                -    1.1 Ghz

·                RAM                 -    256 MB(min)

·                Hard Disk         -   20 GB

·                Floppy Drive    -    1.44 MB

·                Key Board        -    Standard Windows Keyboard

·                Mouse               -    Two or Three Button Mouse

·                Monitor             -    SVGA

SOFTWARE REQUIREMENTS:-

·                Operating System      : AndroidOS                                                     

·                Language                  : Java

·                Database                  : SqLite Database

·                Tool                           :Eclipse

 

REFERENCES

Linares-Vasquez, M., Bernal-Cardenas, C.E., Bavota, G, Di Penta, M. “The Impact of API Change- and Fault-Proneness on the User Ratings of Android Apps” IEEE Transactions on Software Engineering, Volume 41,  Issue 4  November  2014

A Location- And Diversity-Aware News Feed System for Mobile Users

ABSTRACT

A location-aware news feed (LANF) system generates news feeds for a mobile user based on her spatial preference (i.e., her current location and future locations) and non-spatial preference (i.e., her interest). Existing LANF systems simply send the most relevant geo-tagged messages to their users. Unfortunately, the major limitation of such an existing approach is that, a news feed may contain messages related to the same location (i.e., point-of-interest) or the same category of locations (e.g., food, entertainment or sport). We argue that diversity is a very important feature for location-aware news feeds because it helps users discover new places and activities. In this paper, we propose D-MobiFeed; a new LANF system enables a user to specify the minimum number of message categories (h) for the messages in a news feed. In D-MobiFeed, our objective is to efficiently schedule news feeds for a mobile user at her current and predicted locations, such that (i) each news feed contains messages belonging to at least h different categories, and (ii) their total relevance to the user is maximized. To achieve this objective, we formulate the problem into two parts, namely, a decision problem and an optimization problem. For the decision problem, we provide an exact solution by modeling it as a maximum flow problem and proving its correctness. The optimization problem is solved by our proposed three-stage heuristic algorithm. We conduct a user study and experiments to evaluate the performance of D-MobiFeed using a real data set crawled from Foursquare. Experimental results show that our proposed three-stage heuristic scheduling algorithm outperforms the brute-force optimal algorithm by at least an order of magnitude in terms of running time and the relative error incurred by the heuristic algorithm is below 1%. D-MobiFeed with the location prediction method effectively improves the relevance, diversity, and efficiency of news feeds.

                                                                 

 AIM

The aim of this paper is is to efficiently schedule news feeds for a mobile user at her current and predicted locations, such that (i) each news feed contains messages belonging to at least h different categories, and (ii) their total relevance to the user is maximized.

SCOPE

The scope of this paper is to achieve this objective, we formulate the problem into two parts, namely, a decision problem and an optimization problem.

EXISTING SYSTEM

MobiFeed the state-of-the-art location-aware news feed system schedules news feeds for mobile users. In MobiFeed, the relevance of a message m to Bob is measured by both the content similarity between m and Bob’s submitted messages (i.e., a non-spatial factor) and the distance between m and Bob (i.e., a spatial factor). MobiFeed is motivated by the fact that, if the news feeds are only computed based on a user’s location at the query time (i.e., it does not consider the user’s future locations, e.g., GeoFeed), the total relevance of news feeds is not optimized With the geographical distance between a message and a mobile user in a relevance measure model, the relevance of a message to a mobile user is changing as the user is moving. Such a dynamic environment gives us an opportunity to employ location prediction technique to improve the quality of news feeds and the system efficiency. Existing diversification problems focus on retrieving an individual list of items with a certain level of diversity. In contrast, with our location prediction techniques, we aim at improving the quality of news feeds by scheduling multiple location- and diversity-aware news feeds for mobile users simultaneously.

DISADVANTAGES

·      A news feed may contain messages related to the same location (i.e., point-of-interest) or the same category of locations (e.g., food, entertainment or sport).

·      In MobiFeed considers a mobile environment that makes our location- and diversity-aware news feed system unique and more challenging.

PROPOSED SYSTEM

In this project, propose D-MobiFeed; a new LANF system enables a user to specify the minimum number of message categories (h) for the messages in a news feed. In D-MobiFeed, our objective is to efficiently schedule news feeds for a mobile user at her current and predicted locations, such that (i) each news feed contains messages belonging to at least h different categories, and (ii) their total relevance to the user is maximized. To achieve this objective, we formulate the problem into two parts, namely, a decision problem and an optimization problem. For the decision problem, we provide an exact solution by modeling it as a maximum flow problem and proving its correctness. The optimization problem is solved by our proposed three-stage heuristic algorithm. We conduct a user study and experiments to evaluate the performance of D-MobiFeed using a real data set crawled from Foursquare. Experimental results show that our proposed three-stage heuristic scheduling algorithm outperforms the brute-force optimal algorithm by at least an order of magnitude in terms of running time and the relative error incurred by the heuristic algorithm is below 1%. D-MobiFeed with the location prediction method effectively improves the relevance, diversity, and efficiency of news feeds.

 ADVANTAGES

·      D-MobiFeed with the location prediction method effectively improves the relevance, diversity, and efficiency of news feeds.

·      D-MobiFeed can efficiently provide location- and diversity-aware news feeds when maintaining their high quality in terms of relevance

SYSTEM ARCHITECTURE

SYSTEM CONFIGURATION

HARDWARE REQUIREMENTS:-

·                 Processor   -   Pentium –III

·                Speed                -    1.1 Ghz

·                RAM                 -    256 MB(min)

·                Hard Disk         -   20 GB

·                Floppy Drive    -    1.44 MB

·                Key Board        -    Standard Windows Keyboard

·                Mouse               -    Two or Three Button Mouse

·                Monitor             -    SVGA

SOFTWARE REQUIREMENTS:-

·                Operating System      : AndroidOS                                                     

·                Language                   : Java

·                Database                    : SqLite Database

·                Tool                           :Eclipse

REFERENCES

Chow, C.Xu, W. “A Location- and Diversity-aware News Feed System for Mobile Users” IEEE Transactions ON Services Computing, Volume PP, Issue 99 MAY 2015.