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

To the attention of mobile software developers: Guess what, test your app!

Software testing is an important phase in the software development life-cycle because it helps in identifying bugs in a software system before it is shipped into the hand of its end users. There are numerous studies on how developers test general-purpose software applications. The idiosyncrasies of mobile software applications, however, set mobile apps apart from general-purpose systems (e.g., desktop, stand-alone applications, web services). This paper investigates working habits and challenges of mobile software developers with respect to testing. A key finding of our exhaustive study, using 1000 Android apps, demonstrates that mobile apps are still tested in a very ad hoc way, if tested at all. However, we show that, as in other types of software, testing increases the quality of apps (demonstrated in user ratings and number of code issues). Furthermore, we find evidence that tests are essential when it comes to engaging the community to contribute to mobile open source software. We discuss reasons and potential directions to address our findings. Yet another relevant finding of our study is that Continuous Integration and Continuous Deployment (CI/CD) pipelines are rare in the mobile apps world (only 26% of the apps are developed in projects employing CI/CD) --- we argue that one of the main reasons is due to the lack of exhaustive and automatic testing.Comment: Journal of Empirical Software Engineerin

Software Engineering in the Age of App Stores: Feature-Based Analyses to Guide Mobile Software Engineers

Mobile app stores are becoming the dominating distribution platform of mobile applications. Due to their rapid growth, their impact on software engineering practices is not yet well understood. There has been no comprehensive study that explores the mobile app store ecosystem's effect on software engineering practices. Therefore, this thesis, as its first contribution, empirically studies the app store as a phenomenon from the developers' perspective to investigate the extent to which app stores affect software engineering tasks. The study highlights the importance of a mobile application's features as a deliverable unit from developers to users. The study uncovers the involvement of app stores in eliciting requirements, perfective maintenance and domain analysis in the form of discoverable features written in text form in descriptions and user reviews. Developers discover possible features to include by searching the app store. Developers, through interviews, revealed the cost of such tasks given a highly prolific user base, which major app stores exhibit. Therefore, the thesis, in its second contribution, uses techniques to extract features from unstructured natural language artefacts. This is motivated by the indication that developers monitor similar applications, in terms of provided features, to understand user expectations in a certain application domain. This thesis then devises a semantic-aware technique of mobile application representation using textual functionality descriptions. This representation is then shown to successfully cluster mobile applications to uncover a finer-grained and functionality-based grouping of mobile apps. The thesis, furthermore, provides a comparison of baseline techniques of feature extraction from textual artefacts based on three main criteria: silhouette width measure, human judgement and execution time. Finally, this thesis, in its final contribution shows that features do indeed migrate in the app store beyond category boundaries and discovers a set of migratory characteristics and their relationship to price, rating and popularity in the app stores studied

Improving Application Quality using Mobile Analytics

The purpose of this research is to investigate and report on how mobile analytics can help real-world developers improve the quality of their apps efficiently and effectively. The research also considers the effects of mobile analytics in terms of the artefacts developed and maintained by the development team and also researches key characteristics of a range of mobile analytics tools and services. Research Design: the research takes a developer-oriented perspective of using three complementary sources of data: 1) platform-level analytics, using Android Vitals as the primary analytics tool, 2) in-app analytics with a focus on runtime failures caused by crashes and freezes (known as Application Not Responding (ANR) in Android), and 3) interviews with developers. Action research techniques included roles of embedded developer, guide, and observer across different mobile app projects I was involved in. Hackathons were used to experiment with the speed and ability to find and address issues reported by the analytics tools used by the app developers. Their apps have a combined active user base of over 3,000,000 users. Many of these apps use a mainstream crash analytics library which was used to complement and contrast the results provided in the primary analytics tool. The research is intended to facilitate ease of future research and reproducibility, e.g. by using open-source projects as the code, bug reports, etc. are all published and available. This research was complemented by a) collaborating with professional developers who provided additional examples and results, and b) investigating grey material including grey literature and grey data. The findings of this research highlights that using mobile analytics helped to reduce failure rates markedly, quickly, and effectively by applying techniques described here. Various limitations and flaws were found in the analytics tools; these provide cause for concern as they may affect the app’s placement in the app store and revenues. These limitations and flaws also make some issues in the apps harder to identify, prioritise, and fix.We identified ways to compensate for many of these and developed open-source software to facilitate additional analysis. Flaws and bugs were reported to the Android Vitals team at Google who acknowledged they would fix several of them. Several bugs were hard to reproduce, partly as Google deliberately hid pertinent details from the data they gather. Nonetheless app developers were able to ameliorate or fix the bugs for some issues even when they were not able to reproduce them. Android Vitals shows the potential of how the combination of an app store and platform could be used to improve the quality of apps without users needing to actively participate. Some crashes were hard to reproduce and may be impractical to find before the app is released to end users. Developers can determine comparative improvements in their releases, such as whether they fixed a bug, by using Android Vitals and similar analytics tools; i.e. mobile analytics may help teams to determine whether they have improved the quality of their app even with flaws and limitations in the mobile analytics.</i

Self-Reported Activities of Android Developers

To gain a deeper empirical understanding of how developers work on Android apps, we investigate self-reported activities of Android developers and to what extent these activities can be classified with machine learning techniques. To this aim, we firstly create a taxonomy of self-reported activities coming from the manual analysis of 5,000 commit messages from 8,280 Android apps. Then, we study the frequency of each category of self-reported activities identified in the taxonomy, and investigate the feasibility of an automated classification approach. Our findings can inform be used by both practitioners and researchers to take informed decisions or support other software engineering activities.Acknowledgments: European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642954Software Engineerin