Exploring the Prevalence and Evolution of Android Concerns: A Community Viewpoint

Abstract

Abstract:In line with growing awareness of the need for systems to adapt quickly to change, there has been increasing interest in the evolution of software systems. Research has particularly considered developer-led activities change over time. Comparatively less consideration has been given to the study of software evolution as driven by the wider community of stakeholders. Although, a project's wider community is central to the feedback system and project success. We have contributed to such efforts and studied the evolution of architecture issues and non-functional requirements in the Android project, as identified by the wider Android community 1 . We mined the Android issues tracker, employing n-gram analysis in our examination of 21,547 issues. We observe that most architecture-related issues were located in Android application layer, and these issues increased with time. Additionally, usability-related concerns were reported most when they were held to be given greatest attention. Outcomes suggests that Android's open model and shared ownership have positively impacted Google's success, which could provide a useful lesson for other similar projects.

Data-Driven Requirements Elicitation: A Systematic Literature Review

“…Prioritization was supported in the studies [41,48,49,52,58,60,68,72,73,81,[87][88][89]. • Elicitation for change management is to elicit emerging requirements for software evolution, which has been supported in the studies [52,85,87,[97][98][99]102]. • Documentation of quality requirements has been enabled by two studies [88,89], by automatically specifying user stories or semi-formally written requirements in a product backlog, respectively.…”

“…Of the 68 studies included in the analysis, 57 studies (85%) used dynamic data which was externally related to a given organization (i.e., data were collected outside of an organization's platforms) [36-77, 80-82, 86-94, 96, 101]. Nine studies (13%) used dynamic data that were collected from platforms belonging to the organization: issue tracking systems [84,85,102]; user feedback from the online discussion and open-source software mailing lists [79]; sensors equipped with an intelligent product which is also known as a product embedded information devices (PEID) [95]; software production forum [103]; user feedback tool [99]. On the other hand, only two studies (3%) used both internal and external dynamic data [78,100].…”

“…Rule-based classification • Rule-based classification has been applied to identify and classify relevant requirements information, to identify software features [40,41,46,61,62], and to elicit requirements [94]. • Rule-based classification has been performed: using collocation finding algorithms [40,46]; grammar-based and delimiter-based strategies [76]; phrase search [86]; conducting language pattern matching which results are postprocessed with Part-of-Speech tagging [57]; n-gram analysis [102]. It has also been performed based on language patterns [37,61], or linguistic tags [62], propagation rules based on syntactic relations [41], or the SAFE patterns which consist of Part-of-Speech patterns and sentence patterns [94].…”

“…• More detailed classification than filtering has been performed in the studies [35, 37-39, 42, 43, 45, 50, 51, 54-57, 64-66, 79-82, 87, 92, 93, 102-104, 106], for example, by classifying app reviews into bug reports, feature requests, user experiences, or text ratings [38], by classifying user reviews into specific dimensions of usability and user experiences [55], and by classifying app reviews into functional and non-functional requirements [43]. • Classification of requirements-related information has been done using machine-learning approach [35,38,39,42,43,45,50,51,55,56,64,79,92], rule-based classification [37,57,102], and topic modeling [54] Identification of candidate features related to requirements…”

Feature Evolution and Reuse - An Exploratory Study of Eclipse

“…Zaidman et al [10] also used artifacts and the TeMo tool to evaluate the co-evolution of test and production code. From an end-user perspective, Licorish [11] examined the evolution of architecture issues and non-functional requirements in the Android project finding that most architecture-related issues were located in the Android application layer and usability-related concerns were reported most when they were held to be given greatest attention. Further, in an examination of software repository data, Herraiz [12] observed that a small subset of metrics is sufficient to characterize a software system, and software evolution is generally a short-range correlated process.…”