Fine-grained Code Coverage Measurement in Automated Black-box Android Testing

Pilgun, Aleksandr; Gadyatskaya, Olga; Zhauniarovich, Yury; Dashevskyi, Stanislav; Mauw, Sjouke

doi:10.1145/3395042

Cited by 23 publications

(24 citation statements)

References 57 publications

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“…Pilgun's tool repository was published [6] and could be implemented by anyone using a software resource that implies Python, Android SDK, and Java to investigate the smali code coverage through any Android's apk package regarding the classes, methods and instructions. This research team [7] reports a percentage of 96.9 % apps successfully instrumented with ACVTool with a small or not noticeable time overhead. Our research helps readers to investigate code coverage of their Android applications knowing that a similar goal is a path to a better efficiency regarding time consumption when developers build an Android project.…”

Section: Specific Quality Characteristics Of Mlearning Applicationsmentioning

confidence: 98%

Quality Characteristics of Mobile Learning Applications

Mihail-Vaduva¹

2019

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Software quality evaluation for mobile application is a subject with major implications onto educational evaluating process. The place where teacher and his educable person build a temporary social relation is connected to an environment where success is correlated with a mobile-driven mindset. A suitable examination of such mobile learning -environment implies quality characteristics like functional correctness and testability and should be conducted with proper tools if we desire to use mobile learning applications for implementing into a collaborative scenario toward complex classroom events. In this article we are making a raised mark above quality characteristics of mobile learning applications because this is the starting point to acquire a certain comprehension concerning a better utilization of such methods for obtaining knowledge. We construct a potential bridge between two quality characteristics of mobile learning applications to show how to transfer practical results from one quality characteristic toward another to sustain an experimental study with adjacent outline involving mobile programming laboratory curricula. We focus our attention above a software quality metric, the so-called statement code coverage (CC), to obtain a synergic effect between a blackbox approach and a white-box technique in order for developing and testing Android mobile applications. For reaching our desideratum we studied scientific papers regarding the complexity of quality characteristics applied to mobile learning applications, from a general to a particular view, to shed light above previous gathering observations concerning our mission. We have a genuine desire to reveal technical instructions needed to be executed in order to obtain a reproducible pattern of a manually testing process, concerning an Android mobile application, conducted by a beta-testing team of users. he finished with a high school diploma postgraduate training programs named Psychological, Pedagogical and Educational skills training, level I and II, connected with a particular branch of the Bucharest University of Economic Studies. Furthermore, he graduated classes between 1984 and 1989 with a bachelor degree of the Faculty of Technology for Chemistry of the Polytechnic Institute of Bucharest towards specialization concerning Inorganic Chemistry Engineering. Currently he is working as a software analyst within the Department of Information Technology at the Regia Autonomă "Monitorul Oficial" from Bucharest and he is using Oracle database with APEX technology correlated with specific software languages for intranet projects development. His main scientific preoccupation is heavily orientated towards domains situated at the interface between education as a primarily economic and social activity and the most recent software technology.

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Section: Specific Quality Characteristics Of Mlearning Applicationsmentioning

confidence: 98%

Quality Characteristics of Mobile Learning Applications

Mihail-Vaduva¹

2019

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“…Pilgun's tool repository was published [3] and could be implemented by anyone using a software resource that implies Python, Android SDK, and Java to investigate the smali content of any code coverage through any Android's apk package regarding the classes, methods and instructions. This research team [4] reports a percentage of 96.9 % apps successfully instrumented with ACVTool with a small or not noticeable time overhead. Our research helps readers to investigate code coverage of their Android applications knowing that a similar goal is a path to a better efficiency regarding time consumption when developers build an Android project.…”

Section: Introductionmentioning

confidence: 98%

Android App Code Coverage

Mihail-Vaduva¹

2019

Proceedings of the 18th International Conference on INFORMATICS in ECONOMY Education, Research and Business Technologies

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Activities related with software quality evaluation for mobile devices are nowadays confronted with a major outpouring of trends related to outstanding mobile evaluation possibilities. In this paper we use statement code coverage (CC), a software quality metric, in order to discover technical conditions needed to accomplish when we will desire to check and prove that the same pattern of manually testing an Android application was really used, through a beta testing process, by an end user. For this mission we are using the regression analysis. Scientific articles were studied concerning different ways to understand and evaluate calculation of statement code coverage for mobile software application and the relationship between existing methods features and the choice that a developer could have against one or another method to evaluate this quality software metric. Our starting point related with this article, that makes a difference, is motivated by the fact that we use a tool that operates in a black box style, without the need to know the Java Android application's source code. Our quest is orientated in the direction of integrate our practical experiment into an educational laboratory process to a better learning of programming and testing software applications for mobile devices.

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“…Nowadays, there are mainly several criteria [4]- [8] for judging the adequacy of APP testing. First, it is activity coverage.…”

Section: Introductionmentioning

confidence: 99%

“…In previous review papers [11]- [13], many automated testing tools can only use method coverage or activity coverage to judge the test adequacy [14]- [18], to generate the test case [19], to compare test suites [20], to maximize fault detection by prioritizing test cases [4], [21] or to use it as a fitness function to guide application exploration in testing [22]- [24] when testing closed-source APP. Because of the significant differences among methods in Java, it is often too arbitrary to judge whether or not the code in the method is covered by detecting whether or not the method is called.…”

Section: Introductionmentioning

confidence: 99%

Theoretical Analysis and Empirical Evaluation of Coverage Indictors for Closed Source APP Testing

Huang

Hui

2019

IEEE Access

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The number of mobile applications has increased geometrically nowadays, but how to ensure their quality and conduct adequate and effective testing is still a challenge for developers. On the one hand, the number of mobile apps is increasing, and the update speed is faster and faster. Many small and mediumsized companies can hardly test the app adequately before each release. On the other hand, mobile apps play more and more important roles in people's life, such as financial payment. For the sake of company security and user privacy, most companies will encrypt key codes in their APP. Even third-party testers cannot get source code, which also leads to many researchers cannot carry out further research and effective testing for these widely used mainstream APPs. Code coverage is an important indicator to guide software testing, which plays a crucial role in ensuring the quality of testing. However, it is an urgent problem to find accurate coverage indictors to evaluate these tests. And when testing those existing widely used mainstream closed-source apps, we find that the existing coarse-grained coverage metrics like method coverage is bad coverage indictors for app testing that can exaggerate or minimize the actual coverage rate, which cannot obtain satisfactory results for the evaluation of test effects. To find a more reliable coverage indictor, this paper demonstrates the correctness of instruction coverage indictor and the inaccurate of method coverage in evaluating the test of closed-source APP from the perspective of probability and statistics. Then we shows how inaccurate the method coverage can be through an empirical evaluation on datasets of closed source APPs and open source APPs respectively. It is further verified that instruction coverage is a more effective evaluation indictor than methods coverage or activity coverage in the test of closed source APP for the first time. INDEX TERMS Mobile testing, automated testing, coverage criteria, instruction coverage.

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Fine-grained Code Coverage Measurement in Automated Black-box Android Testing

Cited by 23 publications

References 57 publications

Quality Characteristics of Mobile Learning Applications

Quality Characteristics of Mobile Learning Applications

Android App Code Coverage

Theoretical Analysis and Empirical Evaluation of Coverage Indictors for Closed Source APP Testing

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