Edroid: A Mutation Tool for Android Apps

Mutation testing is a well known technique for estimating and improving the quality of test suites. Given a test suite T for a system S, mutation testing systematically creates mutants of S and executes T to measure how many mutants T detects. If T does not detect some (non-equivalent) mutants, T can be improved by adding test cases that detect those mutants. Mutants are created by applying mutation operators. Mutation operators are important because they define the characteristics of the system that are tested as well as the characteristics that are improved in the test suite. While mutation operators are well defined for a number of programming paradigms such as sequential or multi-threaded, to the best of our knowledge, mutation operators have not been defined for the actor programming model. In this paper, we define and classify mutation operators that can be used for mutation testing of actor programs.

A probabilistic framework for mutation testing in deep neural networks

SummaryFor mutation testing, the huge cost of running test suites on a large number of mutants has been a serious obstacle. To resolve this problem, we propose a learning‐based mutant reduction techniqueMuTrain.MuTrainuses cost‐considerate linear regression (i.e., CLARS) to learn amutation model, which predicts the mutation score of a test suite based on the mutation testing results of a previous version of a target program. Then,MuTrainapplies the mutation model for subsequent versions to predict mutation scores with significantly fewer mutants. For effective mutant reduction and accurate mutation score prediction,MuTrainuses fine‐grained mutation operators refined from the existing coarse‐grained mutation operators. The experiment results show thatMuTrainreduces the number of mutants effectively (i.e., selecting only 1.6% of mutants). Moreover,MuTrainpredicts mutation score far more accurately than the existing mutant reduction techniques and random mutant selection. We also found thatMuTrainachieves much greater mutant reduction when it uses the fine‐grained mutation operators than the traditional coarse‐grained mutation operators (i.e., 1.6% vs. 14.6%).

Graphical User Interfaces (GUIs) are amongst the most common user interfaces, enabling interactions with applications through mouse movements and key presses. Tools for automated testing of programs through their GUI exist, however they usually rely on operating system or framework specific knowledge to interact with an application. Due to frequent operating system updates, which can remove required information, and a large variety of different GUI frameworks using unique underlying data structures, such tools rapidly become obsolete, Consequently, for an automated GUI test generation tool, supporting many frameworks and operating systems is impractical. We propose a technique for improving GUI testing by automatically identifying GUI widgets in screen shots using machine learning techniques. As training data, we generate randomized GUIs to automatically extract widget information. The resulting model provides guidance to GUI testing tools in environments not currently supported by deriving GUI widget information from screen shots only. In our experiments, we found that identifying GUI widgets in screen shots and using this information to guide random testing achieved a significantly higher branch coverage in 18 of 20 applications, with an average increase of 42.5% when compared to conventional random testing.

Traditionally, mutation testing is used as test set generation and/or test evaluation criteria once it is considered a good fault model. This paper uses mutation testing for evaluating an automated static analyzer. Since static analyzers, in general, report a substantial number of false positive warnings, the intention of this study is to define a prioritization approach of static warnings based on their correspondence with mutations. On the other hand, knowing that Mutation Test has a high application cost, another possibility is to try to identify mutations of some specific mutation operators, which an automatic static analyzer is not adequate to detect. Therefore, this information can be used to prioritize the order of incrementally applying mutation operators considering, firstly, those with no correspondence with static warnings. In both cases, contributing to the establishment of incremental strategies on using automatic static analysis or mutation testing or even a combination of them. We used mutation operators as a fault model to evaluate the direct correspondence between mutations and static warnings. The main advantage of using mutation operators is that they generate a large number of programs containing faults of different types, which can be used to decide the ones most probable to be detected by static analyzers. We provide evidences on the correspondence between mutations and some types of static warnings. The results obtained for a set of 19 open-source programs indicate that: 1) static warnings may be prioritized based on their correspondence level with mutations; 2) specific set of mutation operators and their mutations may be prioritized based on their correspondence level with warnings. It is possible to provide an incremental testing strategy aiming at reducing the cost of both static analysis and mutation testing using the correspondence information between these activities/artifacts.

Testing is an important activity in engineering of industrial embedded software. In certain application domains (e.g., railway industry) engineering software is certified according to safety standards that require extensive software testing procedures to be applied for the development of reliable systems. Mutation analysis is a technique for creating faulty versions of a software for the purpose of examining the fault detection ability of a test suite. Mutation analysis has been used for evaluating existing test suites, but also for generating test suites that detect injected faults (i.e., mutation testing). To support developers in software testing, we propose a technique for producing test cases using an automated test generation approach that operates using mutation testing for software written in IEC 61131-3 language, a programming standard for safety-critical embedded software, commonly used for Programmable Logic Controllers (PLCs). This approach uses the Uppaal model checker and is based on a combined model that contains all the mutants and the original program. We applied this approach in a tool for testing industrial PLC programs and evaluated it in terms of cost and fault detection. For realistic validation we collected industrial experimental evidence on how mutation testing compares with manual testing as well as automated decision-coverage adequate test generation. In the evaluation, we used manually seeded faults provided by four industrial engineers. The results show that even if mutation-based test generation achieves better fault detection than automated decision coverage-based test generation, these mutation-adequate test suites are not better at detecting faults than manual test suites. However, the mutation-based test suites are significantly less costly to create, in terms of testing time, than manually created test suites. Our results suggest that the fault detection scores could be improved by considering some new and improved mutation operators (e.g., Feedback Loop Insertion Operator (FIO)) for PLC programs as well as higher-order mutations.

Mutation testing is a technique in which faults (mutants) are injected into a program or application to assess its test suite effectiveness. It works by inserting mutants and running the application’s test suite to identify if the mutants are detected (killed) or not (survived) by the tests. Although computationally expensive, it has proven to be an effective method to assess application test suites. Several mutation testing frameworks and tools have been built for the various programing languages, however, very few tools have been built for the JavaScript language, more specifically, there is a lack of mutation testing tools for the Node.js runtime and npm based applications. The npm Registry is a public collection of modules of open-source code for Node.js, front-end web applications, mobile applications, robots, routers, and countless other needs of the JavaScript community. The over 700,000 packages hosted in npm are downloaded more than 5 billion times per week. More and more software is published in npm every day, representing a huge opportunity to share code and solutions, but also to share bugs and faulty software. In this paper, we briefly describe prior work for mutation operators in JavaScript and Node.js, and propose Mutode, an open source tool which leverages the npm package ecosystem to perform mutation testing for JavaScript and Node.js applications. We empirically evaluated Mutode effectiveness by running it on 12 of the top 20 npm modules that have automated test suites.

Semantic mutation testing

Mutation testing is the fault-based software testing approach that is widely applicable for assessing the effectiveness of a test suite. The test suite effectiveness is measured through artificial seeding of faults into the programs under test. Mutation testing tools (MTTs) are significant enablers of the conversion of mutation testing from the research perspective into the real life and mostly applicable testing process. Without using the automatic MTT, mutation testing can’t be really connected in this present reality and is unrealistic to be acknowledged by the industry. Authors analyze six open-source JAVA-based MTT (Jester, JavaMut, MuJava, Jumble, Judy, and Javalanche). The results are based on the performance of various JAVA programs and two real-life applications. Benchmark comparison among the MTT is presented in terms of mutants, mutation operator, mutation score, and quality output. On the basis of comparative analysis, the performance of each tool is explained with the protocol for finding the appropriate tool among the six MTTs. The results show that the MuJava performs best compared to the others.KeywordsMutation testingFault-based testingAdequacy criterionMutation testing tools

Software testing is the dominant method for quality assurance and control in software engineering [1] , [2] . Test suites serve as quality gates to safeguard against programming faults. But not every test suite is written equally. We usually gauge its quality using metrics such as code coverage. These assess how much of the code base has been covered. However, they do not tell if the tests actually test and verify the intentions. Mutation testing does this by deliberately injecting faults into the system under test and verifying how many of them the test suite can detect. For every injected fault that is not detected by the test suite, an additional test should be written. In the academic community, mutation testing is acknowledged as the most promising technique for automated assessment of the strength of a test suite [3] , [4] .

Computation offloading is a promising way to improve the performance as well as reducing the battery power consumption of a smartphone application by executing some parts of the application on a remote server. Supporting such capability is not easy for smartphone application developers due to (1) correctness: some code, e.g., that for GPS, gravity, and other sensors, can run only on the smartphone so that developers have to identify which parts of the application cannot be offloaded; (2) effectiveness: the reduced execution time must be greater than the network delay caused by computation offloading so that developers need to calculate which parts are worth offloading; (3) adaptability: smartphone applications often face changes of user requirements and runtime environments so that developers need to implement the adaptation on offloading. More importantly, considering the large number of today's smartphone applications, solutions applicable for legacy applications will be much more valuable. In this paper, we present a tool, named DPartner, that automatically refactors Android applications to be the ones with computation offloading capability. For a given Android application, DPartner first analyzes its bytecode for discovering the parts worth offloading, then rewrites the bytecode to implement a special program structure supporting on-demand offloading, and finally generates two artifacts to be deployed onto an Android phone and the server, respectively. We evaluated DPartner on three real-world Android applications, demonstrating the reduction of execution time by 46%-97% and battery power consumption by 27%-83%.

SummaryThis article presents the techniques and results of a novel model‐based test case generation approach that automatically derives test cases from UML state machines. The main contribution of this article is the fully automated fault‐based test case generation technique together with two empirical case studies derived from industrial use cases. Also, an in‐depth evaluation of different fault‐based test case generation strategies on each of the case studies is given and a comparison with plain random testing is conducted. The test case generation methodology supports a wide range of UML constructs and is grounded on the formal semantics of Back's action systems and the well‐known input–output conformance relation. Mutation operators are employed on the level of the specification to insert faults and generate test cases that will reveal the faults inserted. The effectiveness of this approach is shown and it is discussed how to gain a more expressive test suite by combining cheap but undirected random test case generation with the more expensive but directed mutation‐based technique. Finally, an extensive and critical discussion of the lessons learnt is given as well as a future outlook on the general usefulness and practicability of mutation‐based test case generation. Copyright © 2014 John Wiley & Sons, Ltd.

Software testing in general and graphical user interface (GUI) testing in particular is one of the major challenges in the lifecycle of any software system. GUI testing is inherently more difficult than the traditional and command-line interface testing. Some of the factors that make GUI testing different from the traditional software testing and significantly more difficult are: a large number of objects, different look and feel of objects, many parameters associated with each object, progressive disclosure, complex inputs from multiple sources, and graphical outputs. The existing testing techniques for the creation and management of test suites need to be adapted/enhanced for GUIs, and new testing techniques are desired to make the creation and management of test suites more efficient and effective. In this article, a methodology is proposed to create test suites for a GUI. The proposed methodology organizes the testing activity into various levels. The tests created at a particular level can be reused at higher levels. This methodology extends the notion of modularity and reusability to the testing phase. The organization and management of the created test suites resembles closely to the structure of the GUI under test.

In modern pervasive applications, it is important to validate access control mechanisms that are usually defined by means of the standard XACML language. Mutation analysis has been applied on access control policies for measuring the adequacy of a test suite. In this paper, we present a testing framework aimed at applying mutation analysis at the level of the Java based policy evaluation engine. A set of Java based mutation operators is selected and applied to the code of the Policy Decision Point (PDP). A first experiment shows the effectiveness of the proposed framework in assessing the fault detection of XACML test suites and confirms the efficacy of the application of code-based mutation operators to the PDP.

Aspect-Oriented Programming (AOP) is a contemporary software development technique that strongly relies on the Separation of Concerns principle. It aims to tackle software modularisation problems by introducing the aspect as a new implementation unit to encapsulate behaviour required to realise the so-called crosscutting concerns. Despite the benefits that may be achieved with AOP, its implementation mechanisms represent new potential sources of faults that should be handled during the testing phase. In this context, mutation testing is a widely investigated fault-based test selection criterion that can help to demonstrate the absence of prespecified faults in the software. It is believed to be an adequate tool to deal with testing-related specificities of contemporary programming techniques such as AOP. However, to date, the few initiatives for customising the mutation testing for aspect-oriented (AO) programs show either limited coverage with respect to the types of simulated faults, or a need for both adequate tool support and proper evaluation. This thesis tackles these limitations by defining a comprehensive mutation-based testing approach for AO programs written in the AspectJ language. It starts with a fault-proneness investigation in order to define a fault taxonomy for AO software. Such taxonomy encompasses a range of fault types and underlay the definition of a set of mutation operators for AO programs. Automated tool support is also provided. A series of quantitative studies show that the proposed fault taxonomy is able to categorise faults identified from several available AO systems. Moreover, the studies show that the mutation operators are able to simulate faults that may not be revealed by pre-existing, non-mutation-based test suites. Furthermore, the effort required to augment the test suites to provide adequate coverage of mutants does not tend to overwhelm the testers. This provides evidence of the feasibility of the proposed approach and represents a step towards the practical fault-based testing of AO programs.