Boost Your 8 Ball Pool Game Play with 3 Line Aim Tool: The Innovative App for Android
We suggest you download a modified version of the game, which will add long lines to the game for more accurate aiming. Please note that the game has good protection and you have to use a special manual to get additional features.
In order to activate long lines mod in addition to 8 Ball Pool, you have to download two apps: Lucky Patcher and Aim Tool For 8 Ball Pool. Lucky Patcher is the most famous program that allows you to modify games and applications independently. Aim Tool is a paid program that allows you to get access to long lines feature for a certain price. We will make it so that you do not have to pay for anything.
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Testing the mod for long lines and auto aim for 8 Ball Pool showed that the declared advantages work absolutely without problems. By completing initial training, you can get access to detailed aiming settings and can easily win matches. Antivirus programs did not detect any threats, so installation file can be considered safe. You can find detailed instructions on installation at the end of this article.
Not solely that, Aim4line for 8 Ball Pool additionally helps superior cushioning shots, consisting of Bank Shot and Kick Shot, so you no longer have to fear about snooker balls. Using the cushion shot, you can without problems in shape the goal ball into every other pocket.
Before executing the test, a snapshot of the start state is taken (line 7). We leverage virtualization to save the visited states and later restore and explore them in App Exploration (detailed in the next subsection). In other words, the AUT is installed and executed on a virtual machine (VM) such as Android Virtual Device  or VirtualBox VM , such that the runtime program state of the AUT, including the underlying OS and emulated hardware, can be stored in a snapshot and fully resumed later. This helps improve the accuracy and performance of GUI state exploration. In prior work [5, 22, 33, 34, 45], a GUI state is resumed by restarting the AUT and replaying the recorded event sequence. However, a shortcoming of this restart-and-replay approach is that the background services and dynamic contents such as timestamps or ads may change after restarting the AUT and the GUI state cannot be reached again. Virtualization addresses this issue. Moreover, the exploration of the AUT can be accelerated, because the snapshots can be restored and processed in parallel with multiple VMs. The practice of using virtualization and snapshots is also adopted by prior work in Android testing .
Test Generation component takes the base path $ p $ and the state transition graph $ G $ as input and generates $ T $, a set of executable tests with size $ n $, as described in Algorithm 3. To that end, in line 2, it retrieves all simple paths in G that (1) are from start state ($ v_s $) to end state ($ v_f $) and (2) visit all oracle states ($ V_o $). We focus on the paths that satisfy these two constraints, because they are required properties for the generated tests (as discussed in Section 2). Moreover, tests with an execution path containing a cycle may include repetitive operations such as navigating back to a previous screen, and hence considered less useful. As a result, the algorithm first considers simple paths (i.e., paths without repeating states) when generating the tests.
If the size of $ T $ is smaller than $ n $ after checking all the simple paths, then Algorithm 3 will further retrieve the execution paths containing only one cycle (lines 4 and 5). Specifically, getCyclicPaths function in line 5 only considers the paths with a non-loop circuit (i.e., a cycle with a length larger than one) on a base state, as illustrated in Figure 4. We only allow this special case for execution paths containing cycles, because we would like to modify the base path conservatively. The retrieved paths are then verified in the same way to generate more tests (line 6). The algorithm returns when the required number of tests are generated or after checking all valid candidate paths (line 6).
With the explored state transition graph of the AUT, the number of candidate paths may grow exponentially and become too many to verify. As a result, Test Generation component prioritizes the candidate execution paths by their difference from the base path (line 10 in Algorithm 3). In other words, we compute a distance score between the base path $ p $ and each candidate path $ p^\prime $ with the pathDiff function in Algorithm 4. The paths with the highest distance scores are executed and verified first.
Algorithm 4 first gets the list of states visited by $ p $ and $ p^\prime $ as $ L $ and $ L^ \prime $, respectively (lines 2 and 3). It then computes a coarse-grained score ($ d_1 $) about how different $ L $ and $ L^\prime $ are in terms of their edit distance (line 4). Here, we treat $ L $ and $ L^\prime $ as two strings and the contained state identifiers (hash values) as characters and compute their Levenshtein distance , i.e., the number of required edits, inserts, or deletions that converts $ L $ to $ L^\prime $. Next, it traverses each state $ v^\prime _i \in L^\prime $ sequentially. If $ v^\prime _i $ is not a base state (i.e., $ v^\prime _i \notin L $), then we compute a fine-grained score to determine how different $ v^\prime _i $ and $ v^\prime _i-1 $ (its previous state) are in terms of the edit distance between their XML representations, i.e., XML tree edit distance (line 8). This helps us estimate if the change from $ v^\prime _i-1 $ to $ v^\prime _i $ is significant or merely incidental (such as a checkbox is selected). Finally, $ d_1 $ and the averaged fine-grained score ($ d_2 $) are normalized into $ [0,1] $ and a weighted sum of them is returned (line 13, with $ \alpha =0.5 $ in our implementation).
To investigate whether the generated tests are advantageous in terms of fault detection effectiveness (FDE), we created mutants for the apps under test with MutApk , an open-source mutation testing tool for APK files. It supports 35 mutation operators designed for Android apps  and performs the mutation on intermediate representations of the code. We created the mutants with MutApk's default strategy, in which both the mutation operators and locations of mutated code were picked randomly. The number of mutants created for each app is shown in Table 4.
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As mentioned previously, the number of tests generated by Route can be configured by setting the variable $ n $. While expanding the test suites may improve their fault detection effectiveness (FDE), it introduces additional costs in terms of test execution and maintenance. We analyzed how the size of augmented test suites influence the FDE in our experiments and listed the results in Table 6. The results indicate that when we grew the test suites linearly, the improvement was sub-linear in terms of both the FDE and the types of covered mutation operators (CMOs). For example, when we expanded the test suites by 2 fold (from 73 tests to 146 tests, by setting $ n=1 $), the FDE improved by a factor of 1.84 ($ 112/61 $). Moreover, in the case that the size of test suites was increased by 4 fold (by setting $ n=3 $), the FDE improved by a factor of 2.26 ($ 138/61 $). Finally, increasing $ n $ from 2 to 3 did not improve the number of covered mutant types. We depict the normalized growth of FDE and CMOs along with the suite size in Figure 12. From this experiment, we conclude configuring Route with a value for $ n $ higher than 3 is likely to result in diminishing returns. Developers may choose to set $ n $ in between 1 and 3, depending on their desire to balance between test suite size and FDE.
App crawling based on GUI hierarchy information. Many automated test generation techniques [5, 7, 8, 14, 15, 22, 32, 33, 34, 36, 38, 45, 48] leverage GUI hierarchy information of the AUT and apply different exploration strategies to create and execute GUI events to improve code coverage. For example, given an Android app, CrashScope  explores the app by dispatching randomly generated events, which can be system events or GUI events extracted from the GUI hierarchy. While we also leverage GUI hierarchy information for test generation, what distinguishes Route from prior work, including CrashScope, is that our exploration strategy is directed by existing test suites to achieve feature-based augmentation. Note that Route is not another record-and-replay tool, such as Reference , because our goal is not to faithfully replay existing tests, but to diverge from them in a manner that allows us to meaningfully examine the same features.
Systematic testing. To perform systematic testing of Android apps, prior work [8, 9, 19, 25, 37] used techniques such as targeted event sequence generation and symbolic execution. Tanzirul and Neamtiu proposed A3E , an app crawler that aims to increase app coverage (Activity and method coverage) by prioritizing events that lead to undiscovered states in the app. Mirzaei et al.  and Anand et al.  introduced concolic testing in Android to automatically explore obscured parts of an app by finding proper events and contextual settings. Jensen et al.  introduced a two-phase technique (generating event-handler summaries and concolic testing) that produce backward event sequences from a target line of code to the entry point of the app. Xiang et al.  tried to address the environmental challenges of symbolic execution in the presence of various Android SDKs and libraries by deducing a representation of libraries on-the-fly.
Similarly, Route tries to find an event sequence to reach a specific part of the app. However, unlike the above tools, our goal is not to increase the code coverage by reaching unexplored parts of an app. In contrast, we try to find a new path covering the already visited states in existing test cases through different sequence of events. In addition, previous tools primarily focus on achieving certain objectives at the source-code level (i.e., to reach specific lines of code), while Route focuses on covering the same behavior and feature as the original test (i.e., to find a path that covers the terminal and oracle GUI states). Finally, it is worth noting that symbolic execution can be used as a complementary approach together with Route to find proper input values to increase the exploration performance.