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

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Published:13 September 2022Publication History
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Abstract

Context: The mobile app market is continually growing offering solutions to almost all aspects of people’s lives, e.g., healthcare, business, entertainment, as well as the stakeholders’ demand for apps that are more secure, portable, easy to use, among other non-functional requirements (NFRs). Therefore, manufacturers should guarantee that their mobile apps achieve high-quality levels. A good strategy is to include software testing and quality assurance activities during the whole life cycle of such solutions.

Problem: Systematically warranting NFRs is not an easy task for any software product. Software engineers must take important decisions before adopting testing techniques and automation tools to support such endeavors.

Proposal: To provide to the software engineers with a broad overview of existing dynamic techniques and automation tools for testing mobile apps regarding NFRs.

Methods: We planned and conducted a Systematic Mapping Study (SMS) following well-established guidelines for executing secondary studies in software engineering.

Results: We found 56 primary studies and characterized their contributions based on testing strategies, testing approaches, explored mobile platforms, and the proposed tools.

Conclusions: The characterization allowed us to identify and discuss important trends and opportunities that can benefit both academics and practitioners.

REFERENCES

  1. [1] Abbasi Abdul Muqtadir, Al-Tekreeti Mustafa, Naik Kshirasagar, Nayak Amiya, Srivastava Pradeep, and Zaman Marzia. 2018. Characterization and detection of tail energy bugs in smartphones. IEEE Access 6 (2018), 6509865108. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Adams Kevin. 2015. Non-functional Requirements in Systems Analysis and Design. Vol. 28. Springer, Cham. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Al-Ahmad A. S., Kahtan H., Hujainah F., and Jalab H. A.. 2019. Systematic literature review on penetration testing for mobile cloud computing applications. IEEE Access 7 (2019), 173524173540. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Almeida Diego R., Machado Patrícia D. L., and Andrade Wilkerson L.. 2019. Testing tools for Android context-aware applications: A systematic mapping. J. Brazil. Comput. Societ. 25, 12 (2019), 122. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Alotaibi Ali, Clause James, and Halfond William G. J.. 2020. Mobile app energy consumption: A study of known energy issues in mobile applications and their classification schemes–summary plan. In Proceedings of the International Conference on Software Maintenance and Evolution (ICSME’20). IEEE, New York, NY, 854854. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  6. [6] Amalfitano Domenico, Riccio Vincenzo, Tramontana Porfirio, and Fasolino Anna Rita. 2020. Do memories haunt you? An automated black box testing approach for detecting memory leaks in Android apps. IEEE Access 8 (2020), 1221712231. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  7. [7] Amin Amr, Eldessouki Amgad, Magdy Menna Tullah, Abdeen Nouran, Hindy Hanan, and Hegazy Islam. 2019. AndroShield: Automated Android applications vulnerability detection, a hybrid static and dynamic analysis approach. Information 10, 10 (2019), 326. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  8. [8] Ampatzoglou Apostolos, Bibi Stamatia, Avgeriou Paris, Verbeek Marijn, and Chatzigeorgiou Alexander. 2019. Identifying, categorizing and mitigating threats to validity in software engineering secondary studies. Inf. Softw. Technol. 106 (2019), 201230. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Avancini Andrea and Ceccato Mariano. 2013. Security testing of the communication among Android applications. In Proceedings of the 8th International Workshop on Automation of Software Test (AST’13). IEEE, New York, NY, 5763. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Banerjee Abhijeet, Chong Lee Kee, Ballabriga Clément, and Roychoudhury Abhik. 2017. EnergyPatch: Repairing resource leaks to improve energy-efficiency of Android apps. IEEE Trans. Softw. Eng. 44, 5 (2017), 470490. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Barra Silvio, Francese Rita, and Risi Michele. 2019. Automating mockup-based usability testing on the mobile device. In Proceedings of the International Conference on Green, Pervasive, and Cloud Computing (GPC’19). Springer, Cham, 128143. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  12. [12] Bhatnagar Shikhar, Malik Yasir, and Butakov Sergey. 2018. Analysing data security requirements of Android mobile banking application. In Proceedings of the International Conference on Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments (ISDDC’18). Springer, Cham, 3037. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] Borys Magdalena and Milosz Marek. 2018. Mobile application usability testing in quasi-real conditions—The synergy of using different methods. In Proceedings of the 11th International Conference on Human System Interaction (HSI’18). IEEE, New York, NY, 362368. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] Bourque Pierre, Fairley Richard E., and Society IEEE Computer. 2014. Guide to the Software Engineering Body of Knowledge (SWEBOK(R)): Version 3.0 (3rd ed.). IEEE Computer Society Press, Washington, DC.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. [15] Cai Haipeng, Zhang Ziyi, Li Li, and Fu Xiaoqin. 2019. A large-scale study of application incompatibilities in Android. In Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis. ACM, New York, NY, 216227. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Caldiera Victor R. Basili, Gianluigi, and Rombach H. Dieter. 1994. The goal question metric approach. Encyc. Softw. Eng.ineering 2 (1994), 528532.Google ScholarGoogle Scholar
  17. [17] Chau Melissa and Reith Ryan. 2020. Smartphone Market Share. Retrieved from https://www.idc.com/promo/smartphone-market-share/os.Google ScholarGoogle Scholar
  18. [18] Cheng Lin Chou. 2016. The mobile app usability inspection (MAUi) framework as a guide for minimal viable product (MVP) testing in lean development cycle. In Proceedings of the 2nd International Conference in HCI and UX. ACM, New York, NY, 111. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Chowdhury Shaiful Alam and Hindle Abram. 2016. GreenOracle: Estimating software energy consumption with energy measurement corpora. In Proceedings of the 13th Working Conference on Mining Software Repositories (MSR’16). ACM, New York, NY, 4960. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Coleti Thiago Adriano, Souza Leticia da Silva, Morandini Marcelo, Allard Suzie, and Correa Pedro Luiz Pizzigatti. 2017. ErgoMobile: A software to support usability evaluations in mobile devices using observation techniques. In Proceedings of the International Conference of Design, User Experience, and Usability (DUXU’17). Springer, Cham, 363378. DOI:.Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Deka Biplab, Huang Zifeng, Franzen Chad, Nichols Jeffrey, Li Yang, and Kumar Ranjitha. 2017. ZIPT: Zero-integration performance testing of mobile app designs. In Proceedings of the 30th Annual Symposium on User Interface Software and Technology (UIST’17). ACM, New York, NY, 727736. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Delamaro Marcio, Jino Mario, and Maldonado Jose. 2016. Introduction to Software Testing (2nd ed.). Elsevier Brasil.Google ScholarGoogle Scholar
  23. [23] Dyba T., Dingsoyr T., and Hanssen G. K.. 2007. Applying systematic reviews to diverse study types: An experience report. In Proceedings of the 1st International Symposium on Empirical Software Engineering and Measurement (ESEM’07). IEEE, New York, NY, 225234. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  24. [24] Espada Ana Rosario, Gallardo María del Mar, Salmerón Alberto, and Merino Pedro. 2017. Performance analysis of Spotify® for Android with model-based testing. Mob. Inf. Syst. 2017 (2017), 114. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Ferre Xavier, Villalba Elena, Julio Héctor, and Zhu Hongming. 2017. Extending mobile app analytics for usability test logging. In Proceedings of the International Conference on Human-Computer Interaction (HCI’17). Springer, Cham, 114131. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Filho Jackson Feijó, Valle Thiago, and Prata Wilson. 2015. Automated usability tests for mobile devices through live emotions logging. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct. ACM, New York, NY, 636643. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  27. [27] Standardization International Organization for. 2013. ISO/IEC/IEEE International Standard—Software and systems engineering – Software testing - -Part 2:Test processes. https://ieeexplore.ieee.org/document/6588543.Google ScholarGoogle Scholar
  28. [28] Guo Chenkai, Xu Jing, Yang Hongji, Zeng Ying, and Xing Shuang. 2014. An automated testing approach for inter-application security in Android. In Proceedings of the 9th International Workshop on Automation of Software Test (AST’14). ACM, New York, NY, 814. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Hay Roee, Tripp Omer, and Pistoia Marco. 2015. Dynamic detection of inter-application communication vulnerabilities in Android. In Proceedings of the International Symposium on Software Testing and Analysis (ISSTA’15). ACM, New York, NY, 118128. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] Henry Gavin. 2021. Matt Lacey on mobile app usability. IEEE Softw. 38, 2 (2021), 134136. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] ISO. 2001. ISO/IEC 9126-1, Software Engineering – Product Quality. ISO, Geneva, Switzerland. https://www.iso.org/standard/22749.html.Google ScholarGoogle Scholar
  32. [32] ISO. 2011. ISO/IEC 25010:2011, Systems and software engineering – Systems and software Quality Requirements and Evaluation (SQuaRE) – System and software quality models. ISO, Geneva, Switzerland. https://www.semanticscholar.org/paper/ISO-%2F-IEC-25010-%3A-2011-Systems-and-software-%E2%80%94-and-%28/ad7102b6bd0842fe1b46ce6a9246b1.f00f51948f.Google ScholarGoogle Scholar
  33. [33] ISO. 2013. ISO/IEC/IEEE 29119-1: Software and systems engineering-Software testing-Part 1: Concepts and definitions. https://ieeexplore.ieee.org/document/6588537.Google ScholarGoogle Scholar
  34. [34] Jabbarvand Reyhaneh, Lin Jun-Wei, and Malek Sam. 2019. Search-based energy testing of Android. In Proceedings of the 41st International Conference on Software Engineering (ICSE’19). IEEE, New York, NY, 11191130. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  35. [35] Jabbarvand Reyhaneh and Malek Sam. 2017. \( \mu \)Droid: An energy-aware mutation testing framework for Android. In Proceedings of the 11th Joint Meeting on Foundations of Software Engineering (ESEC/FSE’17). ACM, New York, NY, 208219. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  36. [36] Jabbarvand Reyhaneh, Mehralian Forough, and Malek Sam. 2020. Automated construction of energy test oracles for Android. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM, New York, NY, 927938. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Janicki Marek, Katara Mika, and Pääkkönen Tuula. 2012. Obstacles and opportunities in deploying model-based GUI testing of mobile software: A survey. Softw. Test., Verif. Reliab. 22, 5 (Aug. 2012), 313341. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Jeong JongWook, Kim NeungHoe, and In Hoh Peter. 2020. Detecting usability problems in mobile applications on the basis of dissimilarity in user behavior. Int. J. Hum.-Comput. Stud. 139 (2020), 102364. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] Junior Misael, Amalfitano Domenico, Garcés Lina, Andrade Stevão, and Delamaro Márcio. 2021. Dataset on Dynamic Testing Techniques of Non-Functional Requirements on Mobile Applications. Retrieved from https://data.mendeley.com/datasets/gswvb2s2ht/3.Google ScholarGoogle Scholar
  40. [40] Kaur Anureet and Kaur Kulwant. 2018. Systematic literature review of mobile application development and testing effort estimation. J. King Saud Univ. Comput. Inf. Sci. 1, 1 (2018), 122. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  41. [41] Keng Joseph Chan Joo, Jiang Lingxiao, Wee Tan Kiat, and Balan Rajesh Krishna. 2016. Graph-aided directed testing of Android applications for checking runtime privacy behaviours. In Proceedings of the 11th International Workshop on Automation of Software Test (AST’16). ACM, New York, NY, 5763. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Ki Taeyeon, Park Chang Min, Dantu Karthik, Ko Steven Y., and Ziarek Lukasz. 2019. Mimic: UI compatibility testing system for Android apps. In Proceedings of the 41st International Conference on Software Engineering (ICSE’19). IEEE, New York, NY, 246256. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] Kim Heejin, Choi Byoungju, and Wong W. Eric. 2009. Performance testing of mobile applications at the unit test level. In Proceedings of the 3rd International Conference on Secure Software Integration and Reliability Improvement (SSIRI’09). Springer, Cham, 171180. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  44. [44] Kitchenham B., Budgen D., and Brereton O. P.. 2010. The value of mapping studies: A participant-observer case study. In Proceedings of the 14th International Conference on Evaluation and Assessment in Software Engineering (EASE’10). 2533. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  45. [45] Kitchenham B. and Charters S.. 2007. Guidelines for performing Systematic Literature Reviews in Software Engineering. https://www.bibsonomy.org/bibtex/23f4b30c0fe1435b642467af4cca120ef/jpmor.Google ScholarGoogle Scholar
  46. [46] Kluth Wolfgang, Krempels Karl-Heinz, and Samsel Christian. 2014. Automated usability testing for mobile applications. In Proceedings of the Web Information Systems and Technologies (WEBIST). 149156. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  47. [47] Knorr Konstantin and Aspinall David. 2015. Security testing for Android mHealth apps. In Proceedings of the 8th International Conference on Software Testing, Verification and Validation Workshops (ICSTW’15). IEEE, New York, NY, 18. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  48. [48] Kong P., Li L., Gao J., Liu K., Bissyandé T. F., and Klein J.. 2019. Automated testing of Android apps: A systematic literature review. IEEE Trans. Reliab. 68, 1 (2019), 4566.Google ScholarGoogle ScholarCross RefCross Ref
  49. [49] Kronbauer Artur H., Santos Celso A. S., and Vieira Vaninha. 2012. Smartphone applications usability evaluation: A hybrid model and its implementation. In Proceedings of the International Conference on Human-Centred Software Engineering (HCSE’12). Springer, Berlin, 146163. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  50. [50] Lanui Ammar and Chiew Thiam Kian. 2019. A cloud-based solution for testing applications compatibility and portability on fragmented Android platform. In Proceedings of the 26th Asia-Pacific Software Engineering Conference (APSEC’19). IEEE, New York, NY, 158164. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  51. [51] Lee Jemin and Kim Hyungshin. 2016. QDroid: Mobile application quality analyzer for app market curators. Mob. Inf. Syst. 2016 (2016), 111. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  52. [52] Lettner Florian and Holzmann Clemens. 2012. Automated and unsupervised user interaction logging as basis for usability evaluation of mobile applications. In Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia (MoMM’12). ACM, New York, NY, 118127. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  53. [53] Li Xueliang, Yang Yuming, Liu Yepang, Gallagher John P., and Wu Kaishun. 2020. Detecting and diagnosing energy issues for mobile applications. In Proceedings of the 29th International Symposium on Software Testing and Analysis. ACM, New York, NY, 115127. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  54. [54] Liang Hongliang, Wang Yudong, Yang Tianqi, and Yu Yue. 2018. AppLance: A lightweight approach to detect privacy leak for packed applications. In Proceedings of the Nordic Conference on Secure IT Systems. Springer, Cham, 5470. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  55. [55] Liu Ao, Xu Jing, Wang Weijing, Yu Jiawei, and Gao Hongcan. 2019. Automated testing of energy hotspots and defects for Android applications. In Proceedings of the International Conference on Energy Internet (ICEI’19). IEEE, New York, NY, 374379. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  56. [56] Liu Chien-Hung. 2019. A compatibility testing platform for Android multimedia applications. Multimedia Tools Applic. 78, 4 (2019), 48854904. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  57. [57] Liu Yi, Wang Jue, Xu Chang, Ma Xiaoxing, and Lü Jian. 2018. NavyDroid: An efficient tool of energy inefficiency problem diagnosis for Android applications. Sci. China Inf. Sci. 61, 5 (2018), 120. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  58. [58] Liu Yang, Zuo Chaoshun, Zhang Zonghua, Guo Shanqing, and Xu Xinshun. 2018. An automatically vetting mechanism for SSL error-handling vulnerability in Android hybrid Web apps. World Wide Web 21 (2018), 127150. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  59. [59] Luo Chu, Goncalves Jorge, Velloso Eduardo, and Kostakos Vassilis. 2020. A survey of context simulation for testing mobile context-aware applications. ACM Comput. Surv. 53, 1 (Feb. 2020). DOI:Google ScholarGoogle ScholarCross RefCross Ref
  60. [60] Ma Xiaoxiao, Yan Bo, Chen Guanling, Zhang Chunhui, Huang Ke, Drury Jill, and Wang Linzhang. 2013. Design and implementation of a toolkit for usability testing of mobile apps. Mob. Netw. Applic. 18 (2013), 8197. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  61. [61] Malan Katherine M., Eloff Jan H. P., and Bruin Jhani A. de. 2018. Semi-automated usability analysis through eye tracking. South Afric. Comput. J. 30 (2018), 6684. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  62. [62] Porras Abel Méndez, López Christian Ulises Quesada, and Coronas Marcelo Jenkins. 2015. Automated testing of mobile applications: A systematic map and review. In Proceedings of the 28th Ibero-American Conference on Software Engineering (CIBSE’15). 114.Google ScholarGoogle Scholar
  63. [63] Muccini H., Francesco A. Di, and Esposito P.. 2012. Software testing of mobile applications: Challenges and future research directions. In Proceedings of the 7th International Workshop on Automation of Software Test (AST’12). IEEE, New York, NY, 2935.Google ScholarGoogle ScholarCross RefCross Ref
  64. [64] Myers Glenford J., Sandler Corey, and Badgett Tom. 2011. The Art of Software Testing (3rd ed.). Wiley Publishing, New York, NY.Google ScholarGoogle Scholar
  65. [65] Naik Kshirasagar, Ali Yasir, Mahinthan Veluppillai, Singh Ajit, and Abogharaf Abdulhakim. 2014. Categorizing configuration parameters of smartphones for energy performance testing. In Proceedings of the 9th International Workshop on Automation of Software Test (AST’14). ACM, New York, NY, 1521. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  66. [66] O’Dea Simon. 2020. Smartphone users worldwide 2016–2021. Retrieved from https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/.Google ScholarGoogle Scholar
  67. [67] Petersen K., Vakkalanka S., and Kuzniarz L.. 2015. Guidelines for conducting systematic mapping studies in software engineering: An update. Inf. Softw. Technol. 64 (2015), 118. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  68. [68] Pressman Roger. 2016. Software Engineering: A Practitioner’s Approach (9th ed.). McGraw-Hill, Inc., USA.Google ScholarGoogle Scholar
  69. [69] Putri Titis Sari and Ramdani Fatwa. 2017. Reliability testing using hybrid exploratory basis of tour and fuzzy Inference System Tsukamoto. In Proceedings of the International Conference on Sustainable Information Engineering and Technology (SIET’17). IEEE, New York, NY, 176183. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  70. [70] Rastogi Vaibhav, Chen Yan, and Enck William. 2013. AppsPlayground: Automatic security analysis of smartphone applications. In Proceedings of the 3rd Conference on Data and Application Security and Privacy (CODASPY’13). ACM, New York, NY, 209220. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  71. [71] Sahinoglu Mehmet, Incki Koray, and Aktas Mehmet S.. 2015. Mobile application verification: A systematic mapping study. In Proceedings of the International Computational Science and Its Applications (ICCSA’15). Springer, Cham, 147163. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  72. [72] Salva Sébastien and Zafimiharisoa Stassia R.. 2015. APSET, an Android aPplication SEcurity testing tool for detecting intent-based vulnerabilities. Int. J. Softw. Tools Technol. Transf. 17, 2 (2015), 201221. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  73. [73] Sequeiros João B. F., Chimuco Francisco T., Samaila Musa G., Freire Mário M., and Inácio Pedro R. M.. 2020. Attack and system modeling applied to IoT, cloud, and mobile ecosystems: Embedding security by design. ACM Comput. Surv. 53, 2 (2020), 25:1–25:32. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  74. [74] Shahriar Hossain, North Sarah, and Mawangi Edward. 2014. Testing of memory leak in Android applications. In Proceedings of the 15th International Symposium on High-Assurance Systems Engineering. IEEE, New York, NY, 176183. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  75. [75] Shi Shangcheng, Wang Xianbo, and Lau Wing Cheong. 2019. MoSSOT: An automated blackbox tester for single sign-on vulnerabilities in mobile applications. In Proceedings of the Asia Conference on Computer and Communications Security (ASIACCS’19. ACM, New York, NY, 269282. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  76. [76] Silva Lady and Lopes Denivaldo. 2020. Model driven engineering for performance testing in mobile applications. In Proceedings of the 5th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM’20). IEEE, New York, NY, 17. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  77. [77] Sommerville Ian. 2011. Software Engineering (9th ed.). Addison-Wesley Publishing Company, USA.Google ScholarGoogle Scholar
  78. [78] Starov Oleksii, Vilkomir Sergiy, Gorbenko Anatoliy, and Kharchenko Vyacheslav. 2015. Testing-as-a-Service for Mobile Applications: State-of-the-Art Survey. Vol. 307. Springer, Cham. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  79. [79] Trahms Carola, Möller Sebastian, and Voigt-Antons Jan-Niklas. 2018. Estimating quality ratings from touch interactions in mobile games. In Proceedings of the 10th International Conference on Quality of Multimedia Experience (QoMEX’18). IEEE, New York, NY, 16. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  80. [80] Tramontana Porfirio, Amalfitano Domenico, Amatucci Nicola, and Fasolino Anna Rita. 2019. Automated functional testing of mobile applications: A systematic mapping study. Softw. Qual. J. 27, 1 (2019), 149201. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  81. [81] Usman Muhammad, Iqbal Muhammad Zohaib, and Khan Muhammad Uzair. 2020. An automated model-based approach for unit-level performance test generation of mobile applications. J. Softw.: Evol. Process 32 (2020), e2215. DOI: .Google ScholarGoogle ScholarCross RefCross Ref
  82. [82] Lee Wesley van der and Verwer Sicco. 2018. Vulnerability detection on mobile applications using state machine inference. In Proceedings of the European Symposium on Security and Privacy Workshops (EuroS&PW’18). IEEE, New York, NY, 110. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  83. [83] Wan Mian, Jin Yuchen, Li Ding, and Halfond William G. J.. 2015. Detecting display energy hotspots in Android apps. In Proceedings of the 8th International Conference on Software Testing, Verification and Validation (ICST’15). IEEE, New York, NY, 110. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  84. [84] Wang Yingjie, Xu Guangquan, Liu Xing, Mao Weixuan, Si Chengxiang, Pedrycz Witold, and Wang Wei. 2020. Identifying vulnerabilities of SSL/TLS certificate verification in Android apps with static and dynamic analysis. J. Syst. Softw. 167 (2020), 110609. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  85. [85] Yang Kun, Zhuge Jianwei, Wang Yongke, Zhou Lujue, and Duan Haixin. 2014. IntentFuzzer: Detecting capability leaks of Android applications. In Proceedings of the 9th Symposium on Information, Computer and Communications Security. ACM, New York, NY, 531536. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  86. [86] Ya’u Badamasi Imam, Salleh Norsaremah, Nordin Azlin, Idris Norbik Bashah, Abas Hafiza, and Alwan Ali Amer. 2019. A systematic mapping study on cloud-based mobile application testing. J. Inf. Commun. Technol. 18, 4 (2019), 485527. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  87. [87] Yu Shengcheng, Fang Chunrong, Yun Yexiao, and Feng Yang. 2021. Layout and image recognition driving cross-platform automated mobile testing. In Proceedings of the IEEE/ACM 43rd International Conference on Software Engineering (ICSE’21). IEEE, New York, NY, 15611571. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  88. [88] Yusop Noorrezam, Kamalrudin Massila, Sidek Safiah, and Grundy John. 2016. Automated support to capture and validate security requirements for mobile apps. In Proceedings of the Asia Pacific Requirements Engineering Conference (APSEC’16). Springer, Singapore, 97112. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  89. [89] Zein Samer, Salleh Norsaremah, and Grundy John. 2016. A systematic mapping study of mobile application testing techniques. J. Syst. Softw. 117, C (July 2016), 334356. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  90. [90] Zhang Hailong, Wu Haowei, and Rountev Atanas. 2016. Automated test generation for detection of leaks in Android applications. In Proceedings of the 11th International Workshop on Automation of Software Test. ACM, New York, NY, 6470. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  91. [91] Zhang Tao, Gao Jerry, Cheng Jing, and Uehara Tadahiro. 2015. Compatibility testing service for mobile applications. In Proceedings of the Symposium on Service-Oriented System Engineering (SOSE’15). IEEE, New York, NY, 179186. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  92. [92] Zhu Chenyang, Zhu Zhengwei, Xie Yunxin, Jiang Wei, and Zhang Guiling. 2019. Evaluation of machine learning approaches for Android energy bugs detection with revision commits. IEEE Access 7 (2019), 8524185252. DOI:Google ScholarGoogle ScholarCross RefCross Ref

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        cover image ACM Computing Surveys
        ACM Computing Surveys  Volume 54, Issue 10s
        January 2022
        831 pages
        ISSN:0360-0300
        EISSN:1557-7341
        DOI:10.1145/3551649
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        Publication History

        • Published: 13 September 2022
        • Online AM: 12 January 2022
        • Accepted: 20 December 2021
        • Revised: 29 October 2021
        • Received: 28 January 2021
        Published in csur Volume 54, Issue 10s

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