Characterizing the evolution of statically-detectable performance issues of Android apps

Mobile apps are playing a major role in our everyday life, and they are tending to become more and more complex and resource demanding. Because of that, performance issues may occur, disrupting the user experience or, even worse, preventing an effective use of the app. Ultimately, such problems can cause bad reviews and influence the app success. Developers deal with performance issues thorough dynamic analysis, i.e., performance testing and profiler tools, albeit static analysis tools can be a valid, relatively inexpensive complement for the early detection of some such issues. This paper empirically investigates how potential performance issues identified by a popular static analysis tool — Android Lint — are actually resolved in 316 open source Android apps among 724 apps we analyzed. More specifically, the study traces the issues detected by Android Lint since their introduction until they resolved, with the aim of studying (i) the overall evolution of performance issues in apps, (ii) the proportion of issues being resolved, as well as (iii) the distribution of their survival time, and (iv) the extent to which issue resolution are documented by developers in commit messages. Results indicate how some issues, especially related to the lack of resource recycle, tend to be more frequent than others. Also, while some issues, primarily of algorithmic nature, tend to be resolved quickly through well-known patterns, others tend to stay in the app longer, or not to be resolved at all. Finally, we found how only 10% of the issue resolution is documented in commit messages

Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques

Code comments are considered an efficient way to document the functionality of a particular block of code. Code commenting is a common practice among developers to explain the purpose of the code in order to improve code comprehension and readability. Researchers investigated the effect of code comments on software development tasks and demonstrated the use of comments in several ways, including maintenance, reusability, bug detection, etc. Given the importance of code comments, it becomes vital for novice developers to brush up on their code commenting skills. In this study, we initially investigated what types of comments novice students document in their source code and further categorized those comments using a machine learning approach. The work involves the initial manual classification of code comments and then building a machine learning model to classify student code comments automatically. The findings of our study revealed that novice developers/students’ comments are mainly related to Literal (26.66%) and Insufficient (26.66%). Further, we proposed and extended the taxonomy of such source code comments by adding a few more categories, i.e., License (5.18%), Profile (4.80%), Irrelevant (4.80%), Commented Code (4.44%), Autogenerated (1.48%), and Improper (1.10%). Moreover, we assessed our approach with three different machine-learning classifiers. Our implementation of machine learning models found that Decision Tree resulted in the overall highest accuracy, i.e., 85%. This study helps in predicting the type of code comments for a novice developer using a machine learning approach that can be implemented to generate automated feedback for students, thus saving teachers time for manual one-on-one feedback, which is a time-consuming activity

A Quantitative and Qualitative Investigation of Performance-Related Commits in Android Apps

Performance is nowadays becoming a crucial issue for mobile apps, as they are often implementing computational-intensive features, are being used for mission-critical tasks, and, last but not least, a pleasant user experience often is a key factor to determine the success of an app. This paper reports a study aimed at preliminarily investigating to what extent developers take care of performance issues in their commits, and explicitly document that. The study has been conducted on commits of 2,443 open source Android apps, of which 180 turned out to contain a total of 457 documented performance problems. We classified performance-related commits using a card sorting approach, and found that the most predominant kinds of performance-related changes include GUI-related changes, fixing code smells, network-related code, and memory management

End Users’ Perspective of Performance Issues in Google Play Store Reviews

The success of mobile applications is closely tied to their performance which shapes the user experience and satisfaction. Most users often delete mobile apps from their devices due to poor performance indicating a mobile app’s failure in the competitive market. This paper performs a quantitative and qualitative analysis and investigates performance-related issues in Google Play Store reviews. This study has been conducted on 368,704 reviews emphasizing more 1- and 2-star reviews distributed over 55 Android apps. Our research also reports a taxonomy of 8 distinct performance issues obtained using manual inspection. Our findings show that end-users recurrently raised Updation (69.11%), Responsiveness (25.11%), and Network (3.28%) issues among others. These results can be used as preliminary steps towards understanding the key performance concerns from the perspective of end users. Furthermore, Our long-term objective will be to investigate whether developers resolve these performance issues in their apps.peerReviewe

Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques

Code comments are considered an efficient way to document the functionality of a particular block of code. Code commenting is a common practice among developers to explain the purpose of the code in order to improve code comprehension and readability. Researchers investigated the effect of code comments on software development tasks and demonstrated the use of comments in several ways, including maintenance, reusability, bug detection, etc. Given the importance of code comments, it becomes vital for novice developers to brush up on their code commenting skills. In this study, we initially investigated what types of comments novice students document in their source code and further categorized those comments using a machine learning approach. The work involves the initial manual classification of code comments and then building a machine learning model to classify student code comments automatically. The findings of our study revealed that novice developers/students’ comments are mainly related to Literal (26.66%) and Insufficient (26.66%). Further, we proposed and extended the taxonomy of such source code comments by adding a few more categories, i.e., License (5.18%), Profile (4.80%), Irrelevant (4.80%), Commented Code (4.44%), Autogenerated (1.48%), and Improper (1.10%). Moreover, we assessed our approach with three different machine-learning classifiers. Our implementation of machine learning models found that Decision Tree resulted in the overall highest accuracy, i.e., 85%. This study helps in predicting the type of code comments for a novice developer using a machine learning approach that can be implemented to generate automated feedback for students, thus saving teachers time for manual one-on-one feedback, which is a time-consuming activity.peerReviewe