CN110209593B - A preference-oriented Android application testing method - Google Patents
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Abstract
本发明提出了一种面向偏好设置的安卓应用测试方法。首先对待测应用的可执行文件进行静态程序分析，以得到应用中定义的所有偏好设置与其属性。随后在已有测试用例上，通过动态程序分析的方式获得各测试用例中相关联的偏好设置。对于每条测试用例，可测试所有相关偏好设置与其可能输入的组合，来实现面向偏好设置的测试。最后为了降低这一测试的成本，本发明提供了一种目标导向的执行模式，它将测试用例的代码分割为基本块，将基本块与其相关的偏好设置的输入组合作为测试目标。依照贪心算法，本发明选取覆盖最多的测试目标的测试用例与偏好设置的组合进行测试，从而减少各测试用例下重复的偏好设置输入组合的测试，大大降低测试成本。
The invention provides a preference setting-oriented Android application testing method. The executable file of the application under test is first subjected to static program analysis to obtain all preferences and properties defined in the application. Then, on the existing test cases, the preference settings associated with each test case are obtained by means of dynamic program analysis. For each test case, all combinations of the relevant preference settings and their possible inputs can be tested to implement preference-oriented testing. Finally, in order to reduce the cost of this test, the present invention provides a goal-oriented execution mode, which divides the code of the test case into basic blocks, and uses the basic block and the input combination of its associated preference settings as the test target. According to the greedy algorithm, the present invention selects the combination of the test case covering the most test target and the preference setting for testing, thereby reducing the repeated testing of the preference setting input combination under each test case, and greatly reducing the test cost.
Description
Technical Field
The invention belongs to the field of software testing and program analysis, and mainly relates to a preference setting-oriented android application testing method.
Background
Android, as a mobile platform occupying more than 80% of the market share of the current mobile intelligent terminal, the application of android on the mobile intelligent terminal becomes an integral part of daily life of people. Due to the characteristics of different android application requirements and short development period, sufficient application testing is difficult to perform. Therefore, a large number of test automation execution frameworks are proposed, and testers can execute the application to be tested and collect execution information after compiling test cases, so that the test automation execution frameworks are widely used in the industry at present. On the basis, the academics also put forward a plurality of automatic test input generation technologies, which automatically generate test cases to be executed on the application through a random model-based search-based strategy to achieve the test effect.
However, no automated testing research has focused on the preference settings on android applications, resulting in a great lack of testing of the preference settings. Preference settings, i.e., options defined in an application, that allow a user to modify to change the application appearance or functionality according to personal preferences. To test the preference settings completely, it is necessary to test the combination of all preference settings at all input values. Furthermore, the association of the preference settings with the application interfaces/functions is implicit, so all interfaces/functions in the application need to be tested in all combinations (usually in the form of test cases). The resulting testing costs for this combination are immense.
Reduction of unnecessary/inefficient test combinations by program analysis techniques is a common means of reducing test costs for this problem. Program analysis, i.e. analyzing the internal operation flow of a program written in a certain language. It can be mainly divided into two strategies, static analysis and dynamic analysis, the former analyzes the source code/object code of the program without running the computer program, and the latter analyzes the program behavior by obtaining the program running information.
In view of this problem of the preference setting, the program analysis technology can obtain the preference setting defined in the android application through static analysis, then obtain the preference setting that does not play a role in the program running process under each test case through dynamic analysis, and reject it in the test combination. In addition, the accuracy of the dynamic analysis can be improved, so that unnecessary and covered preference setting input combinations are further eliminated, and the test cost is reduced.
Disclosure of Invention
The invention aims to provide a preference setting-oriented android application testing method, which is used for testing preference settings as comprehensively as possible with the lowest testing cost as possible based on given test cases, so that the testing effect is further improved.
In order to achieve the purpose, the method has the technical scheme that the method for testing the android application oriented to the preference setting is characterized by mainly comprising the following steps of:
step 1, performing static analysis on an android application to be tested to obtain preference settings and attributes thereof defined in the application, and recording a set of all the preference settings as PI;
step 2, carrying out dynamic analysis on a given test case to obtain associated preference settings under each test case, and enumerating all relevant preference settings under the test case and all possible input combinations of the preference settings, wherein any combination is called a test scene, and a set formed by all test scenes is marked as PS;
and 3, in the target-oriented execution mode, dividing the application code into basic blocks, analyzing the basic blocks contained in each test case and the preference setting associated with each basic block, so as to decompose the PS into the combination of the preference setting input and the basic blocks, namely the test targets, the set of which is marked as PB, and continuously selecting the test scene covering the most test targets for execution.
Further, preference settings are defined in the android application in the form of resource files, and these resource files are mapped in the logic code to GUI interfaces by calling the android API interface, thereby allowing the user to modify these preference settings. Therefore, in the static analysis, the preference setting defined in the application and the attribute contained in the preference setting are identified through the obtained resource file, and the identified preference setting set is the PI.
Further, the static analysis, in addition to identifying the preference setting, also confirms the GUI interface where the preference setting included in the corresponding resource file is located through the calling position of the API interfaces by constructing the calling relationship of the application, so as to perform setting operation on the preference setting in the subsequent execution mode.
Further, the dynamic analysis is to obtain an execution track of the code in the execution of each test case by means of instrumentation, and then obtain the preference setting associated with the test case by symbolically reproducing this track. Then, all combinations of these preference settings and their possible inputs are exhausted, and the set formed by these combinations under all test cases is PS.
Further, in the android application, the preference setting uses a key value storage mechanism of SharedPreferences and uses a specific value of an attribute key (referred to as a key value for short) as a key to read and store. Therefore, when the execution tracks of the test cases are symbolically reproduced, the dynamic analysis symbolizes the parameters of all SharedPreferences reading operations in the tracks, and takes the calculation result as the key value to find the elements with the same key value in the PI: if yes, the preference setting corresponding to the element is associated with the test case; if not, the read operation is skipped.
Further, the relevant preference settings obtained in the analysis of each test case in the dynamic analysis are rejected if the relevant preference settings do not play a role in case execution after reading, that is, the relevant preference settings are not used for judging branch selection in the conditional expression of the conditional branch.
Further, the possible input values of preference setting in dynamic analysis may come from resource file acquisition of the static analysis application; if the resource file does not contain the input value, the dynamic analysis randomly gives an input value according to the data type set by the preference.
Further, the basic blocks in the target-oriented execution mode represent a sequence of statements executed sequentially in the test application code program, with only one entry and one exit, and only branch statements can appear at the exit or entry positions.
Furthermore, in the target-oriented execution mode, when the relevance between the basic block and the preference setting is analyzed, the way of analyzing the relevance between the test case and the preference setting can be used, and the relevance result is divided according to the basic block; then, under a certain basic block, the test of a group of input combinations of associated preference settings, called test targets, exhaustively enumerates the set formed by all test targets, namely PB.
Further, the execution mode of the target guide comprises the following specific steps:
step 31, cutting PB according to a test target which is achieved in the execution of the original test case;
step 32, searching a certain test scenario in the PS, wherein the uncovered test targets contained in the test scenario, namely the test targets still contained in the PB are the most, and performing combined execution of preference setting input and an original test case according to the test scenario;
step 33, obtaining the test target achieved in the execution of the previous step;
step 34, if the test targets which have not been reached before are reached, the PB is reduced according to the test targets; otherwise, cutting down the PB according to all the test targets in the execution, and putting the test targets into a retry list;
step 35, repeating the steps 32-34 until PB is empty;
and step 36, recovering the state from the PS to the state after the step 32, finding the test scene which contains the test target and has the least test target from the PS for each test target in the retry list, and repeatedly executing the test scene.
Further, in the specific steps 31 and 36 of the guided execution mode, when there are a plurality of combinations found, different selection strategies may be used, including all the combinations being selected, and the three types are selected preferentially and randomly according to the validity of the test case.
Further, in the specific steps 31 and 34 of the guided execution mode, according to the reduction of the test targets in the PB, these test targets are also deleted for the test scenarios in the PS, and the preference setting inputs related only to these test targets in the scenarios are deleted.
Further, the joint execution in the specific step 32 of the guided execution mode includes the following steps:
step 321, taking out a set of preference settings and corresponding inputs from the test scenario in order, and executing step 322 and 323 until all preference settings and corresponding inputs in the combination are executed;
step 322, opening a GUI (graphical user interface) where the preference setting is located, which is obtained according to static analysis, by means of an ADB (Adadb) tool provided by an Android platform;
step 323, finding the specific position on the GUI interface according to the attribute of the preference setting, and completing the modification of the preference setting according to the type and the given input;
and 324, executing the original test case.
Compared with the prior art, the method makes up the defect of poor test effect of the preference setting part in the current mainstream test method, and initiatively provides a test method specially aiming at preference setting of android application for the test case only to test the preference setting input combination related to the test case;
secondly, the invention selects the combination of the test case covering the most test targets and the preference setting for testing, thereby reducing the test of repeated preference setting input combination under each test case and greatly reducing the test cost;
furthermore, the present invention starts from a given test case and is therefore compatible with other test methods, including automated test input generation methods and manual testing: for the former, the invention can effectively improve the test effect, and for the latter, the test case writer can reduce or even not need to write the test case with preference setting of the part of functions under the support of the invention, thereby greatly reducing the test cost.
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Fig. 1 is a schematic flow chart of the preference setting-oriented android application testing method according to the embodiment.
Fig. 2 is a detailed flow chart of static analysis of the preference setting-oriented android application testing method according to the embodiment.
Fig. 3 is a detailed flowchart of dynamic analysis of the preference setting-oriented android application testing method according to this embodiment.
Fig. 4 is a specific flowchart of a target-oriented execution mode of the preference setting-oriented android application testing method according to this embodiment.
Detailed Description
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
As shown in fig. 1, a schematic flow chart of the preference setting-oriented android application testing method of this embodiment is shown, and is also a general flow chart of this method, which includes three steps:
step 101, performing static analysis on an executable file of an application to be tested, namely an APK file, to obtain all preference settings defined in the application, wherein a set of the preference settings is marked as PI and is used for assisting the analysis of the next step 102;
102, analyzing the associated preference settings in each test case in a mode of dynamically executing the test cases on the application to be tested, thereby enumerating combinations of all the relevant preference settings and possible inputs thereof under all the test cases, wherein the total set is marked as PS;
and 103, calculating the test targets which are possibly reached by the combination under each PS through a target-oriented execution mode, continuously selecting the combination with the most test targets from the PS, namely, the combination which can bring the maximum promotion effect to the test for execution, and reducing the PS according to the execution result until the PS is empty.
As shown in fig. 2, this is a specific flow chart of static analysis of the preference setting-oriented android application testing method of this embodiment, and static program analysis is performed on an executable file of an application to be tested to obtain all preference settings and attributes thereof defined in the application, i.e., a set PI, and specific steps are as follows:
step 201, acquiring a resource file of an executable file, namely an APK (android package) file, of an application to be tested through an apktool or a jadx reverse tool;
in step 202, find the files for describing the preference settings, and read the preference settings and their attributes, including key (for uniquely marking the preference settings), title (for displaying text on the interface), postblevalue (all possible values), and catelog (type).
Step 203, analyzing and obtaining all functions defined in the android program to be tested and call relations thereof from the APK file with the help of a Java analysis tool Soot, thereby constructing a function call relation graph (including implicit calls therein);
step 204, finding out the call of the specific android API function which loads and maps the resource files into the GUI interface in all functions, and performing traversal selection;
step 205, in the graph constructed in step 203, all functions directly and indirectly containing the call are searched;
step 206, if there is a lifecycle function of a certain Activity in the functions, the resource file mapped in the call is loaded and mapped to the GUI interface corresponding to the Activity, in this case, step 207 is skipped; otherwise, it indicates that the resource file is not really loaded, and jumps to step 208;
step 207, adding the Activity information as a new attribute location for all the preference settings contained in the loaded resource file, where the attribute represents the location of the GUI interface where the preference settings are located.
Step 208, searching whether calls of the specific android API functions which are not traversed exist, and if yes, returning to the step 204 to continue circulation; and if not, jumping out, wherein a set formed by all the preference settings and the attributes contained in the preference settings is the PI.
As shown in fig. 3, this is a specific flowchart of dynamic analysis of the preference setting-oriented android application testing method according to this embodiment, and a process of dynamically analyzing the preference setting associated with each test case through the analyzed preference setting set PI and a given test case, and finally generating a set PS includes the following specific steps:
step 301, inserting a recorder, namely a pile, into the application executable file, and then installing the application executable file into the android device;
step 302, selecting an unexecuted test case, executing the test case on the instrumented application, and then obtaining an execution track of an application code in the test case execution from a recorder;
step 303, constructing a linear program statement execution sequence according to the trajectory; this step and the above step 301 can be completed with the help of a Java analysis tool, root;
step 304, selecting sentences in sequence to perform symbolization execution;
step 305, in symbolic execution, if the statement includes a call to a read operation function of sharedpreference, if the statement includes the call, the step 306 is skipped; if not, go to step 309.
Step 306, symbolically calculating the parameter in the reading operation, and taking the calculation result as a key value to search preference settings with the same key value in the PI;
step 307, if the corresponding preference setting can be found, skipping to step 308; if not, return to step 309;
step 308, replacing the output value of the reading operation with a specific symbol representing the preferred setting, and participating in the subsequent symbolization execution and calculation;
step 309, checking whether the statement is a branch statement, if yes, jumping to step 310; otherwise, jumping to step 313;
step 310, symbolically calculating the conditional expression of the branch statement;
step 311, if the numerical values obtained by the symbolic calculation include specific symbols representing specific preference settings, it indicates that the values of the preference settings are to be used for determining the branch selection, i.e. play a role in the test case execution, and then go to step 312; otherwise, jumping to step 313;
step 312, regarding the preference setting playing a role in the test case, identifying that the preference setting is associated with the test case, and recording;
313, judging whether a next statement exists, and if so, returning to 304 for circulation; otherwise, jumping out to step 313;
step 314, judging whether a test case which is not executed exists or not, if so, jumping to the step 302 for circulation; if not, jumping to step 315;
step 315, enumerating all relevant preference settings and combinations of possible inputs (the posblevalue attribute of the preference setting, which is randomly given according to the data type when the attribute is empty) of all test cases, and finally obtaining a set as PS.
Fig. 4 is a specific flowchart of a target-oriented execution mode of the preference setting-oriented android application testing method according to this embodiment, and the specific steps are as follows:
step 401, in the above dynamic analysis process, by dividing the execution stream by using the basic block as a unit, calculating all test targets included in a certain test scenario, that is, in a certain basic block, a set of related preference settings and combinations of input values thereof;
step 402, during dynamic execution, obtaining an execution track of a test case, wherein the execution track includes branch selection in the execution of the original test case, so that a test target which is reached in the original test can be calculated, and a PB is reduced according to the test target, so that the PB only includes test targets which are not reached in the original test;
step 403, judging whether PB is empty, and if not, skipping to step 404 to start a loop; if the state is empty, the loop is stopped, and the process jumps to 408;
step 404, selecting a test scenario with the most test targets from the PS for execution, and if there is a combination with the most test targets, randomly selecting one of the test scenarios for execution;
step 405, checking whether any test target is covered according to the execution result, and if so, jumping to step 406; if not, go to step 407;
step 406, now covering any test targets, so these will be deleted from the PB;
step 407, at this time, any test target is not covered, so that these test targets with coverage failure are not only deleted from PB, but also need to be added into the failure list;
deleting the test targets in the steps 402, 406 and 407, and also deleting the test targets in the corresponding test scenes in the PS, and deleting preference setting input only related to the test targets in the scenes;
step 408, recovering the state from the PS to the step 402;
step 409, selecting a test target from the failure list;
step 410, selecting a combination from the PS to execute, wherein the combination should include the test target and the number of the test targets is the minimum, and if there are a plurality of such combinations, randomly selecting one of the combinations to execute;
step 411, checking whether there are unselected test targets in the failure list, if yes, skipping to step 409 to repeat the cycle; if not, the execution mode of the target guide is ended.
In addition, the execution of steps 404 and 410 according to a given test scenario, as also shown on the right side of fig. 4, includes the following specific steps:
step e401, traversing the preference setting and the corresponding input in the selection combination;
step e402, opening the GUI interface where the android ADB tool is located according to the location attribute set by the preference; then, positioning the specific position of the preference setting title on the interface according to the preference setting title attribute;
step e403, modifying the preference setting according to the specific input;
step e404, checking whether the preference setting and the input which are not selected exist, if so, skipping to e401 to repeat the cycle; if not, jumping out of the cycle to e 405;
and e405, executing the original test case.
The above description is only a preferred embodiment of the present invention, and should not be taken as limiting the invention in any way, and any insubstantial modifications or equivalent changes made by the technical spirit of the present invention without departing from the scope of the present invention are intended to be covered by the claims of the present invention.
Claims (6)
1. A test method for android application facing preference setting is characterized by comprising the following steps:
step 1, performing static analysis on an android application to be tested to obtain preference settings and attributes thereof defined in the application, and recording a set of all the preference settings as PI;
step 2, carrying out dynamic analysis on a given test case to obtain associated preference settings under each test case, and enumerating all relevant preference settings under the test case and all possible input combinations of the preference settings, wherein any combination is called a test scene, and a set formed by all test scenes is marked as PS; the dynamic analysis comprises the following steps: obtaining a code execution track in each test case execution in a pile inserting mode, then, analyzing and obtaining preference setting associated with the test case by symbolically reproducing the track; then, a next group of preference settings of a test case and a corresponding input test combination are called as a test scenario, and a set formed by exhausting all the test scenarios is a PS; in the android application, the preference setting uses a key value storage mechanism of shared preference setting SharedPreferences, and uses the specific value of the attribute key as a key to read and store, so when symbolizing and reproducing the execution track of each test case, the dynamic analysis symbolizes and calculates all parameters of SharedPreferences reading operation in the track, and uses the calculation result as the specific value of key to search the element with the same specific value of key in the PI: if yes, the preference setting corresponding to the element is associated with the test case; if not, skipping the read operation;
step 3, in a target-oriented execution mode, dividing the application code into basic blocks, analyzing the basic blocks contained in each test case and the preference setting associated with each basic block, so as to decompose the PS into the combination of the preference setting input and the basic blocks, namely a test target, the set of which is marked as PB, and continuously selecting a test scene covering the most test targets for execution;
the execution mode of the target guide comprises the following specific steps:
step 31, cutting PB according to a test target which is achieved in the execution of the original test case;
step 32, searching a test scenario in the PS, wherein the test scenario contains the largest number of uncovered test targets, namely the test targets still contained in the PB, and performing combined execution of preference setting input and an original test case according to the test scenario;
step 33, obtaining the test target achieved in the execution of the previous step;
step 34, if the test targets which have not been reached before are reached, the PB is reduced according to the test targets; otherwise, cutting down the PB according to all the test targets in the execution, and putting the test targets into a retry list;
step 35, repeating the steps 32-34 until PB is empty;
step 36, recovering the state of the PS after the step 32, searching each test target in the retry list from the PS for a test scene which contains the test target and has the least test target, and repeatedly executing;
the joint execution in step 32 comprises the following steps:
step 321, taking a group of preference settings and corresponding inputs from the test scenario in order, and executing the following steps 322-323 until all preference settings and corresponding inputs in the combination are executed;
step 322, opening the GUI interface where the preference setting obtained according to the static analysis is located, and searching the specific position of the preference setting on the GUI interface according to the attribute of the preference setting;
step 323. the modification of the preference setting is done according to its type and given input;
and 324, executing the original test case.
2. The method for testing the android application oriented to the preference setting of claim 1, wherein the step 1: in the android application, preference settings are defined in the form of resource files, and specific android API functions are called in logic codes to load and map the resource files into GUI interfaces, so that a user is allowed to modify the preference settings, therefore, in the static analysis process, the preference settings defined in the application and the attributes contained in the application are identified through the resource files obtained by applying source codes or applying executable files reversely, and a preference setting set obtained by identification is a PI;
the static analysis is used for identifying the preference setting, analyzing the byte code of the application executable file, constructing a function calling relation in the application code, finding out the calling position of the specific API function from the function calling relation, and confirming the GUI interface where the preference setting contained in the corresponding resource file is located, so that the preference setting can be set in a subsequent execution mode.
3. The method for testing the android application oriented to the preference setting of claim 1, comprising: in the dynamic analysis, for the relevant preference setting obtained in each test case analysis, if the relevant preference setting does not play a role in case execution after reading, namely is not used for judging branch selection in the conditional expression of the conditional branch, the relevant preference setting is removed.
4. The method for testing the android application oriented to the preference setting of claim 1, comprising: the possible input values of the preference setting in the dynamic analysis are obtained through a resource file of the static analysis application; if the resource file does not contain the input value, an input value is randomly given according to the data type set by the preference.
5. The method for testing the android application oriented to the preference setting of claim 1, comprising: the basic block represents a sequence of statements executed sequentially in the test application code program, with only one entry and one exit, and only branch statements can appear at the exit or entry positions.
6. The method for testing the android application oriented to the preference setting of claim 1, wherein the step 3: when the relevance between the basic block and the preference setting is analyzed, the way of analyzing the relevance between the test case and the preference setting is used, and the relevance result is divided according to the basic block; then, under a basic block, a set of associated tests of input combinations of preference settings, called test targets, exhaustively enumerates the set of all test targets, which is PB.
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