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See detailAutomated Testing of Android Apps: A Systematic Literature Review
Kong, Pingfan UL; Li, Li; Gao, Jun UL et al

in IEEE Transactions on Reliability (2018)

Automated testing of Android apps is essential for app users, app developers and market maintainer communities alike. Given the widespread adoption of Android and the specificities of its development ... [more ▼]

Automated testing of Android apps is essential for app users, app developers and market maintainer communities alike. Given the widespread adoption of Android and the specificities of its development model, the literature has proposed various testing approaches for ensuring that not only functional requirements but also non-functional requirements are satisfied. In this paper, we aim at providing a clear overview of the state-of-the-art works around the topic of Android app testing, in an attempt to highlight the main trends, pinpoint the main methodologies applied and enumerate the challenges faced by the Android testing approaches as well as the directions where the community effort is still needed. To this end, we conduct a Systematic Literature Review (SLR) during which we eventually identified 103 relevant research papers published in leading conferences and journals until 2016. Our thorough examination of the relevant literature has led to several findings and highlighted the challenges that Android testing researchers should strive to address in the future. After that, we further propose a few concrete research directions where testing approaches are needed to solve recurrent issues in app updates, continuous increases of app sizes, as well as the Android ecosystem fragmentation. [less ▲]

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See detailA Closer Look at Real-World Patches
Liu, Kui UL; Kim, Dongsun UL; Koyuncu, Anil UL et al

Scientific Conference (2018, September)

Bug fixing is a time-consuming and tedious task. To reduce the manual efforts in bug fixing, researchers have presented automated approaches to software repair. Unfortunately, recent studies have shown ... [more ▼]

Bug fixing is a time-consuming and tedious task. To reduce the manual efforts in bug fixing, researchers have presented automated approaches to software repair. Unfortunately, recent studies have shown that the state-of-the-art techniques in automated repair tend to generate patches only for a small number of bugs even with quality issues (e.g., incorrect behavior and nonsensical changes). To improve automated program repair (APR) techniques, the community should deepen its knowledge on repair actions from real-world patches since most of the techniques rely on patches written by human developers. Previous investigations on real-world patches are limited to statement level that is not sufficiently fine-grained to build this knowledge. In this work, we contribute to building this knowledge via a systematic and fine-grained study of 16,450 bug fix commits from seven Java open-source projects. We find that there are opportunities for APR techniques to improve their effectiveness by looking at code elements that have not yet been investigated. We also discuss nine insights into tuning automated repair tools. For example, a small number of statement and expression types are recurrently impacted by real-world patches, and expression-level granularity could reduce search space of finding fix ingredients, where previous studies never explored. [less ▲]

Detailed reference viewed: 45 (14 UL)