An automated model‐based approach for unit‐level performance test generation of mobile applications

Abstract

Mobile devices have limited resources, including memory and processing speed. The performance of mobile applications is an important concern. There are a large number of mobile platforms available with varying operating systems and hardware. Native applications are usually developed and maintained separately for these platforms. The overall performance of native applications may significantly vary across platforms. The current industrial practice is to manually test the performance for each variant, which is not a scalable or efficient approach. We tackled the problem of generating native application variants in our previous work. This paper proposes an automated model‐based approach for performance test generation for native application variants at unit level. We propose a performance profile that allows modeling of domain‐specific performance parameters on UML models, which are used for automated performance test generation for each native variant. The results of applying the approach on two real‐world applications show that the approach evaluates the performance of application variants for two different versions of Android successfully and have potential to reduce the effort and time. A questionnaire‐based study is conducted to evaluate the usefulness of the approach.

Dynamic Testing Techniques of Non-functional Requirements in Mobile Apps: A Systematic Mapping Study

“…Therefore, we intend to include a speciic investigation from static testing. NoName ✓ N/A S2 [81] NoName ✓ N/A S3 [7] AndroShield ✓ ✓ ✓ https://github.com/AmrAshraf/AndroShield S4 [70] AppsPlayground ✓ N/A S5 [38] NoName ✓ N/A S6 [6] FunesDroid ✓ ✓ https://github.com/reverse-unina/FunesDroid S8 [42] Mimic ✓ N/A S9 [75] MoSSOT…”

“…The need to test how apps impact battery/power consumption [34], [35], [65], [83], [10], [1], [92], [19], [43], [76], [53] Performance bugs are very diicult to be detected and reproduced [57], [24], [51] Performance testing is a very time-consuming task [43] Lack of tools or methodologies for performance testing [34], [35], [83], [1], [36] The need to detect memory leaks [6] Poor performance apps negatively impact the user experience [6], [51], [90] App performance may vary diferent mobile platforms [81], [76] Developers do not have proper knowledge about performance, therefore they are not careful with this q.c [81], [6], [21], [19] S Developers do not have proper knowledge about security, therefore they are not careful with this q.c [7], [70], [75], [28], [58], [12], [72], [88], [29], [41], [47], [9], [82], [84], [54],…”

“…Finite state machines (FSMs) modeling the behavior of the entire app [72], combinations of use cases and user interface models [88], FSMs modeling UI behavior [24], models or metamodels capable of characterizing performance aspect [76,81].…”

“…All the UB approaches are of OP subcategory and execute test cases comprising sequences of user events to generate log iles reporting performance and consumption of the apps [6,21,43,83]. MB approaches generate test cases either from FSM describing the app behavior [24,34] or FS able to characterize performance aspects [76,81]. The other approaches either belong to the ET subcategory of SEIE, which test the app performance by exploring the UI on the ly [51], or are of MT subcategory of FB injecting energy mutants into the app source code [35].…”

“…PE S U P R C Year Venue S1 [50] A cloud-based solution for testing applications compatibility and portability on fragmented android platform ✓ ✓ 2019 Conference S2 [81] An automated model-based approach for unit-level performance test generation of mobile applications ✓ 2020 Journal S3 [7] Androshield: automated android applications vulnerability detection, a hybrid static and dynamic analysis approach ✓ 2019 Journal S4 [70] Appsplayground: automatic security analysis of smartphone applications ✓ 2013 Conference S5 [38] Detecting usability problems in mobile applications on the basis of dissimilarity in user behavior ✓ 2020 Journal S6 [6] Do memories haunt you? an automated black box testing approach for detecting memory leaks in android apps ✓ 2020 Journal S7 [79] Estimating quality ratings from touch interactions in mobile games ✓ 2018 Conference S8 [42] Mimic: ui compatibility testing system for android apps ✓ 2019 Conference S9 [75] Mossot: an automated blackbox tester for single sign-on vulnerabilities in mobile applications ✓ 2019 Conference S10 [57] NavyDroid: an eicient tool of energy ineiciency problem diagnosis for Android applications ✓ 2018 Journal S11 [24] Performance analysis of spotify (r) for android with model-based testing ✓ 2017 Journal S12 [34] Search-based energy testing of android ✓ 2019 Conference S13 [61] Semi-automated usability analysis through eye tracking ✓ 2018 Journal S14 [49] Smartphone applications usability evaluation: a hybrid model and its implementation ✓ 2012 Conference S15 [35] µDroid: an energy-aware mutation testing framework for Android ✓ 2017 Symposium S16 [28] An automated testing approach for inter-application security in android ✓ 2014 Workshop S17 [58] [20] ErgoMobile: A software to support usability evaluations in mobile devices using observation techniques ✓ 2017 Conference S28 [41] Graph-aided Directed Testing of Android Applications for Checking Runtime Privacy Behaviours ✓ 2016 Workshop S29 [13] Mobile application usability testing in quasi-real conditions-the synergy of using diferent methods ✓ 2018 Conference S30 [47] Security testing for Android mHealth apps ✓ 2015 Workshop S31 [9] Security testing of the communication among Android applications ✓ 2013 Workshop S32 [18] The mobile app usability inspection (MAUi) framework as a guide for minimal viable product (MVP) testing in lean development cycle ✓ AH Tests are already im...…”