Software Testing Techniques Research Articles

Sistem Pakar Diagnosa Penyakit ISPA dengan Metode Forward Chaining

The development of computer technology today continues to experience many changes every year, which are often developed by artificial intelligence, such as expert system technology. An expert system is a computer-based application that can match or imitate the ability of an expert used to solve problems that cannot be solved by ordinary people. His knowledge is taken from books, experience and knowledge. Children often experience ISPA disease caused by viruses and bacteria because children's immune systems are still susceptible to being different from adults. This disease usually begins with a hot body temperature accompanied by symptoms such as sore throat or painful swallowing, runny nose, dry cough and others. So that many parents do not know the symptoms of ISPA disease, as for some ways to prevent ISPA disease are diligently washing hands, increasing consumption of foods containing vitamins, exercise. To make it easier for parents to detect ISPA disease, the authors made this study using the forward chaining method, using this method the resulting system is a system that provides a choice of several symptoms then based on the selected symptoms conclusions will be drawn. This ARI disease expert system uses blackbox testing because blackbox testing is a software testing technique that focuses on the functional specifications of the software or system. Blackbox testing is done on each submenu view, input, edit, print, delete data. So that it will produce an expert system to diagnose ISPA in children online

On the Faults Found in REST APIs by Automated Test Generation

RESTful web services are often used for building a wide variety of enterprise applications. The diversity and increased number of applications using RESTful APIs means that increasing amounts of resources are spent developing and testing these systems. Automation in test data generation provides a useful way of generating test data in a fast and efficient manner. However, automated test generation often results in large test suites that are hard to evaluate and investigate manually. This article proposes a taxonomy of the faults we have found using search-based software testing techniques applied on RESTful APIs. The taxonomy is a first step in understanding, analyzing, and ultimately fixing software faults in web services and enterprise applications. We propose to apply a density-based clustering algorithm to the test cases evolved during the search to allow a better separation between different groups of faults. This is needed to enable engineers to highlight and focus on the most serious faults. Tests were automatically generated for a set of eight case studies, seven open-source and one industrial. The test cases generated during the search are clustered based on the reported last executed line and based on the error messages returned, when such error messages were available. The tests were manually evaluated to determine their root causes and to obtain additional information. The article presents a taxonomy of the faults found based on the manual analysis of 415 faults in the eight case studies and proposes a method to support the classification using clustering of the resulting test cases.

Smoke testing for machine learning: simple tests to discover severe bugs

Machine learning is nowadays a standard technique for data analysis within software applications. Software engineers need quality assurance techniques that are suitable for these new kinds of systems. Within this article, we discuss the question whether standard software testing techniques that have been part of textbooks since decades are also useful for the testing of machine learning software. Concretely, we try to determine generic and simple smoke tests that can be used to assert that basic functions can be executed without crashing. We found that we can derive such tests using techniques similar to equivalence classes and boundary value analysis. Moreover, we found that these concepts can also be applied to hyperparameters, to further improve the quality of the smoke tests. Even though our approach is almost trivial, we were able to find bugs in all three machine learning libraries that we tested and severe bugs in two of the three libraries. This demonstrates that common software testing techniques are still valid in the age of machine learning and that considerations how they can be adapted to this new context can help to find and prevent severe bugs, evenin mature machine learning libraries.

BW-TOPSIS: A Hybrid Method to Evaluate Software Testing Techniques

Software testing plays a significant role in various software development phases. There are so many software testing techniques available. Selecting the most suitable software testing technique based on multiple factors is challenging for software practitioners. This paper proposes an MCDM-based hybrid approach for selecting the most appropriate software testing technique among various available software testing techniques, considering multiple factors such as cost, schedule, resources, etc. Because of the involvement of multiple factors, the problem of selecting the most appropriate software testing technique can be modeled as an MCDM problem. This study proposes a hybrid approach by integrating two MCDM methods BWM (Best-Worst Method) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), for evaluating various software testing techniques considering multiple factors altogether. For the applicability of the proposed approach, an experimental study was conducted using seven software testing techniques and six evaluation criteria. Results show the proposed approach can be used as an efficient tool for selecting the most suitable software testing technique among various available testing techniques in the presence of multiple factors.

Modified Harris Hawks Optimization Based Test Case Prioritization for Software Testing

Generally, software testing is considered as a proficient technique to achieve improvement in quality and reliability of the software. But, the quality of test cases has a considerable influence on fault revealing capability of software testing activity. Test Case Prioritization (TCP) remains a challenging issue since prioritizing test cases is unsatisfactory in terms of Average Percentage of Faults Detected (APFD) and time spent upon execution results. TCP is mainly intended to design a collection of test cases that can accomplish early optimization using preferred characteristics. The studies conducted earlier focused on prioritizing the available test cases in accelerating fault detection rate during software testing. In this aspect, the current study designs a Modified Harris Hawks Optimization based TCP (MHHO-TCP) technique for software testing. The aim of the proposed MHHO-TCP technique is to maximize APFD and minimize the overall execution time. In addition, MHHO algorithm is designed to boost the exploration and exploitation abilities of conventional HHO algorithm. In order to validate the enhanced efficiency of MHHO-TCP technique, a wide range of simulations was conducted on different benchmark programs and the results were examined under several aspects. The experimental outcomes highlight the improved efficiency of MHHO-TCP technique over recent approaches under different measures.

The Art, Science, and Engineering of Fuzzing: A Survey

Among the many software testing techniques available today, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuzzing</i> has remained highly popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of empirical evidence in discovering real-world software vulnerabilities. At a high level, fuzzing refers to a process of repeatedly running a program with generated inputs that may be syntactically or semantically malformed. While researchers and practitioners alike have invested a large and diverse effort towards improving fuzzing in recent years, this surge of work has also made it difficult to gain a comprehensive and coherent view of fuzzing. To help preserve and bring coherence to the vast literature of fuzzing, this paper presents a unified, general-purpose model of fuzzing together with a taxonomy of the current fuzzing literature. We methodically explore the design decisions at every stage of our model fuzzer by surveying the related literature and innovations in the art, science, and engineering that make modern-day fuzzers effective.

Different Approaches To Black box Testing Technique For Finding Errors

Software testing is the process of analyzing software to find the difference between required and existing condition. Software testing is performed throughout the development cycle of software and it is also performed to build quality software, for this purpose two basic testing approaches are used, they are white box testing and black box testing. One of the software testing technique which I have explain in my paper is Black Box Testing, it is a method of generating test cases that are independent of software internal structure, I have also briefly explore various different approaches to black box testing technique for finding errors. Since black box testing is always based either directly or indirectly on the software specification so it is also called specification based testing.

An Automated Testing Framework for Gesture Recognition System using Dynamic Image Pattern Generation with Augmentation

In the field of information technology, the gesture recognition system plays a very essential role. As it has achieved vast importance, it is mandatory to test the recognition system to ensure the quality of the system by identifying the bugs in the software. In our research, we suggested a dynamic testing method for gesture recognition software. using dynamic image pattern generation with augmentation. The automated software testing framework is a set of processes to create new test cases for properly testing a image processing software. The research intention for generate automated testing cases by following a standard process which helps to increase the performance and efficiency of the gesture recognition system. We have built the framework to give proper testing and give result (accuracy and defect) for which gesture recognition system already in the market. our research, the team strongly following and adding two software testing standard. First one is ISO/IEC/IEEE/291129-3 to define the process for testing software. And the second one is ISO/IEC/IEEE/291129-5 to implement the techniques for software testing. We proposed this framework with major five parameters by noise, rotation, background, contrast, and scale. Which are the most use with every gesture recognition system. Our developed framework’s phase is used to generate new testing cases based on the existing gesture recognition system’s data. There are we work with five systems, commonly with the gesture recognition for experiments. We provide the testing report with total accuracy and defect by comparing existing well-known system’s data. At the final result, our system suggested an analysis report based on the testing result. And tell what are the improvement needs for the existing system to consider noised images or different scaled images to build a robust system.&#x0D; GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 42-50

Hybrid swarm intelligence-based software testing techniques for improving quality of component based software

Being a time-consuming and costly activity, software testing always demands optimization and automation. Software testing is an important activity to achieve quality and customer satisfaction. This paper presents a comparative evaluation of different hybrid automated software testing techniques using the concepts of soft computing for overall quality enhancement. A comparison between three hybrid automation techniques is carried out i.e., hybrid ant colony optimization-genetic algorithms (ACO-GA), hybrid artificial bee colony (ABC)-Naïve Bayes, hybrid ABC-GA along with three parent approaches. The comparison is made by applying these hybrid techniques for the selection of minimized test suites thus reducing overall testing effort and eliminating useless or redundant test cases. The experimental results prove the efficiency of these hybrid approaches in different scenarios. The impact of automated testing techniques for quality enhancement is assessed in terms of defect density and defect detection percentage.

An Empirical Comparison of t-GSC and ACO_TCSP Applied to Time Bound Test Selection

Background: Test case selection is a highly researched and inevitable software testing activity. Various kinds of optimization approaches are being used for solving the test selection and prioritization problem. Greedy approach and search based techniques have already been applied to address test case selection. A greedy approach to solve set cover problem is proposed to find a minimal test suite that is able to detect maximum faults. Search based approximation technique - Ant Colony Optimization reduces and prioritizes the test suite to form an optimized test suite. Time bound test case prioritization is an NP-complete problem. Mutation testing is used to inculcate known faults into the programs under test to generate effective test cases. Objective: To empirically evaluate the performance of a greedy approach and a search based approach for test case selection. Methods: This paper attempts to compare a search based approximation approach against a time sensitive greedy approach for test suite optimization using mutation testing. The proposed greedy approach for yielding optimized test suite within a time bound environment is implemented in Python. The two techniques used, search based and greedy approach have also been empirically compared on 24 programs in different languages (C, C#, and Java and Python). Result: The greedy approach has shown better complexity over the ACO approach and the same is validated by experimentation using the actual run time of the algorithms. The time bound greedy approach yielded best result for 16 out of the 24 programs. The ACO approach could find the best result 100% times for 11 programs and at least 30-95% times for rest 13 programs by running ACO 10 times on each program. Yet the percentage reductions achieved in the size and execution time of the resultant test suite were almost similar in both the techniques. Conclusion: The results inspire the further use of both the techniques in software testing.

Next-Generation Software Verification: An AI Perspective

In recent years, automated software verification has progressed significantly. We can now effectively explore complex software structures through automated testing or to prove properties of complex programs, such as compilers using formal methods. But, for the most part, software testing and formal software verification techniques have advanced independently with relatively few insights on how their research thrusts compare or can be combined.

SOFTWARE QUALITY CONTROL MANAGEMENT USING BLACK-BOX TESTING ON AN EXISTING WEBSHOP TRINITISHOP

Creating the need for continuous growth and progress of all involved and connected business entities. They require compliance with certain web standards and support for multiple different browsers and devices. To be convinced of the version of the published code, we need a team that loves challenges, has the creativity and a desire for constant learning. Code, as a basis for a successful business, must be written appropriately with minimal deficiencies in logic and writing. The correctness and validity of the production code mostly depend on the program team itself and its responsibility for the written code. Vital code with errors can produce serious problems and unforeseen consequences. To achieve complete accuracy of all parts of the written code, it is necessary to use software testing and QA technique - quality assurance. The focus of the work is on the application and writing of the necessary programming code that uses QA/QC and the black-box method of testing the existing webshop. After analysis, the authors conclude that software quality control will be a constant challenge for many companies to survive in the short term of the adjustment process and that the crisis is an opportunity for courageous companies to invest ambitiously and timely in their internet business to become market leaders.

Role of Machine Learning &amp; Artificial Intelligence Techniques in Software Testing

This study provides information about the use of artificial intelligence (AI) and machine learning (ML) techniques in the field of software testing. The use of AI in software testing is still in its initial stages. Also the automation level is lesser compared to more evolved areas of work.AI and ML can be used to help reduce tediousness and automate tasks in software testing. Testing can be made more efficient and smarter with the help of AI. Researchers recognize potential of AI to bridge the gap between human and machine driven testing capabilities. There are still number of challenges to fully utilize AI and ML techniques in testing but it will definitely enhance the entire testing process and skills of testers and will contribute in business growth. Machine learning research is a subset of overall AI research. The life-cycle of software is increasingly shortening and becoming more complicated. There is a struggle in software development between the competing pressures of developing software and meeting deadlines. AI-powered automated testing makes conducting full test suites in a timely manner on every change. In this article a detailed overview about the various applications of AI in software testing have been demonstrated. Also the implementation of machine learning in software testing has been discussed in detail and use of different machine learning techniques has been explained as well.

Comparative Analysis of Software Testing Techniques for Mobile Applications

Software testing is the investigation process required to give sufficient information about the quality of the program and its conformity with the specifications that have been identified by the customer and to find problems and errors before they are discovered when the system or program is run by the user. It has an important role in software engineering, which is essential to Software Quality Assurance (SQA). Due to increasing numbers of mobile applications users, testing of mobile application is an essential phase in developing any application to ensure that software has zero defects. For that, many software testing techniques and tools are developed, each having its own features to test software. This paper describes and gives general overview of different strategies of software testing techniques that commonly used for detecting errors in mobile applications, which are: White box testing, Black box testing, Gray box testing, Mutation testing, Regression testing and Fuzzy testing. The primary goal of this paper is to emerge the effective technique of the mobile application testing. As a result of the research, most of testing techniques are currently used in mobile testing include Mutation, Regression and Fuzzy testing. The White box testing and Black box testing techniques are rarely used. Also, the latest research into mobile application testing focuses on GUI testing of mobile applications.

Enhancement of Mutation Testing via Fuzzy Clustering and Multi-Population Genetic Algorithm

Mutation testing, a fundamental software testing technique, which is a typical way to evaluate the adequacy of a test suite. In mutation testing, a set of mutants are generated by seeding the different classes of faults into a program under test. Test data shall be generated in the way that as many mutants can be killed as possible. Thanks to numerous tools to implement mutation testing for different languages, a huge amount of mutants are normally generated even for small-sized programs. However, a large number of mutants not only leads to a high cost of mutation testing, but also make the corresponding test data generation a non-trivial task. In this paper, we make use of intelligent technologies to improve the effectiveness and efficiency of mutation testing from two perspectives. A machine learning technique, namely fuzzy clustering, is applied to categorize mutants into different clusters. Then, a multi-population genetic algorithm via individual sharing is employed to generate test data for killing the mutants in different clusters in parallel when the problem of test data generation as an optimization one. A comprehensive framework, termed as <inline-formula><tex-math notation="LaTeX">$\mathbf {FUZGENMUT}$</tex-math></inline-formula> , is thus developed to implement the proposed techniques. The experiments based on nine programs of various sizes show that fuzzy clustering can help to reduce the cost of mutation testing effectively, and that the multi-population genetic algorithm improves the efficiency of test data generation while delivering the high mutant-killing capability. The results clearly indicate that the huge potential of using intelligent technologies to enhance the efficacy and thus the practicality of mutation testing.

Development of Software Testing Techniques for Early Fault Detection

The study in the paper instigates about the techniques of finding the early fault finding in the software. The research work is associated with the fact of finding the problems in analytical way and by taking the action ranging from automation to the code identification to parallel implementation to code redundancy etc. The result of the study releases the multiple factors in finding the different stages of testing. That may lead to the quality, flexibility and good quality of production. The research shows that there are more problems found at the test point. Most of the research study is based on CTD - Combinatorial Test Design (CTD) techniques. One of the most popular CTD is OATS which is Orthogonal Array Based Testing Strategy, which decides on variable interactions.

Automating and Optimizing Software Testing using Artificial Intelligence Techniques

The final product of software development process is a software system and testing is one of the important stages in this process. The success of this process can be determined by how well it accomplishes its goal. Due to the advancement of technology, various software testing tools have been introduced in the software engineering discipline. The use of software is increasing day-by-day and complexity of software functions are challenging and there is need to release the software within the short quality evaluation period, there is a high demand in adopting automation in software testing. Emergence of automatic software testing tools and techniques helps in quality enhancement and reducing time and cost in the software development activity. Artificial Intelligence (AI) techniques are widely applied in different areas of Software engineering (SE). Application of AI techniques can help in achieving good performance in software Testing and increase the productivity of the software development firms. This paper briefly presents the state of the art in the field of software testing by applying AI techniques in software testing.

Literature review on software testing techniques and test suite reduction frameworks/tools

Testers scrutinise the software application with respect to user and technical requirements. Software testing tends to become a chaotic process when it is performed on a daily basis. The test case generation is an employment-intensive task and it is unachievable to accomplish all the test cases. In this paper various test suite reduction (TSR) methods are discussed to solve the test suite size problem heuristically. Over the past few years, numerous test suite reduction (TSR) frameworks/tools are formulated to minimise the testing effort and to test with least execution time within the limited resource. The main motive of this paper is to elaborate various testing techniques and to emphasise on TSR related work to highlight the critical aspects. The study discloses that the generic TSR framework independent of any programming field and TSR framework for agile environment should be formulated. Finally, the research challenges and issues are presented for future exploration.

Systematic Fuzz Testing Techniques on a Nanosatellite Flight Software for Agile Mission Development

The success of CubeSat space missions depends on the ability to perform properly in a harsh environment. A key component in space missions is the flight software, which manages all of the processes executed by the satellite on its onboard computer. Literature shows that CubeSat missions suffer high infant mortality, and many spacecraft failures are related to flight software errors, some of them resulting in complete mission loss. Extensive operation testing is the primary technique used by CubeSats developers to ensure flight software quality and avoid such failures. The “New Space” requirements pressure to add “agility” to the software development, which could limit the capacity to test. While advanced and beneficial software testing techniques are found in the software engineering field, CubeSat software solutions mostly rely on unit testing, software in the loop simulation, and hardware in the loop simulation. In this work, fuzz testing techniques were developed, implemented, and evaluated as a manner to expedite operational testing of CubeSats while maintaining their completeness. The impact of the tools was evaluated by using the three new 3U CubeSats under development at the University of Chile. We identified twelve bugs not covered by classic testing strategies in less than three days. These failures were reported, fixed, and characterized by the developers in eight sprint sessions. Our results indicate that fuzz testing improved the completeness of flight software testing through automation and with almost no development interruption. Although our approach has been tested on the SUCHAI flight software, it applies to systems that follow a similar architecture.

A Literature Review on Software Testing Techniques for Smartphone Applications

Smartphone applications are getting popular and have become a necessity. There numerous smartphone applications ranging from entertaining to gaming and from utility to mission-critical. Almost everything on the web is now in hands of Smartphone users, which makes this domain very important and its quality should not be compromised. Achieving the desired quality is not an easy task for the mobile platform as it has its limitations. To produce a quality app, developers and testers need to test and assess the app in numerous ways to ensure the best trait of the application. In this concern, some efficient and mature techniques are required to test smartphone applications. In this study, the techniques, approaches, and models to assess mobile apps covering major prospects and angels to test mobile apps are identified. Our focus is on assessing the existing techniques and to evaluate them on standard validation parameters.