Software Testing Process Research Articles

Requirements for creating tools to emulate software integration processes using HTTP/HTTPS protocols

Emulation of software systems integration processes using HTTP/HTTPS protocols is now widely used in the development of distributed systems software systems. The paper discusses the current state of the problem, analyzes the existing tools to solve it, identifies shortcomings, and offers a conceptual approach and program requirements that will automate and debug software development and testing processes using HTTP/HTTPS protocols.

Adaptive Random Testing in Detecting Layout Faults of Web Applications

As part of a software testing process, output verification poses a challenge when the output is not numeric or textual, such as graphical. The industry practice of using human oracles (testers) to observe and verify the correctness of the actual results is both expensive and error-prone. In particular, this practice is usually unsustainable when developing web applications — the most popular software of our era. This is because web applications change frequently due to the fast-evolving requirements amid popular demand. To improve the cost effectiveness of browser output verification, in this study we design failure-based testing techniques and evaluate the effectiveness and efficiency thereof in the context of web testing. With a novel application of the concept of adaptive random sequence (ARS), our approach leverages peculiar characteristics of failure patterns found in browser layout rendering. An empirical study shows that the use of failure patterns and inclination to guide the testing flow leads to more cost-effective results than other classic methods. This study extends the application of ARSs from the input space of programs to their output space, and also shows that adaptive random testing (ART) can outperform random testing (RT) in both failure detection effectiveness (in terms of F-measure) and failure detection efficiency (in terms of execution time).

Research Notes: Binary Test-Suites Using Covering Arrays

Software testing is an essential activity to ensure the quality of software systems. Combinatorial testing is a method that facilitates the software testing process; it is based on an empirical evidence where almost all faults in a software component are due to the interaction of very few parameters. The test generation problem for combinatorial testing can be represented as the construction of a matrix that has certain properties; typically this matrix is a covering array. Covering arrays have a small number of tests, in comparison with an exhaustive approach, and provide a level of interaction coverage among the parameters involved. This paper presents a repository that contains binary covering arrays involving many levels of interaction. Also, it discusses the importance of covering array repositories in the construction of better covering arrays. In most of the cases, the size of the covering arrays included in the repository reported here are the best upper bounds known, moreover, the files containing the matrices of these covering arrays are available to be downloaded. The final purpose of our Binary Covering Arrays Repository (BCAR) is to provide software testing practitioners the best-known binary test-suites.

Development and Pilot Evaluation of Smartphone-Delivered Cognitive Behavior Therapy Strategies for Mood- and Anxiety-Related Problems: MoodMission

Given the ubiquity and interactive power of smartphones, there are opportunities to develop smartphone applications (apps) that provide novel, highly accessible mental health supports. This paper details the development of a smartphone app, “MoodMission,” that aims to provide evidence-based Cognitive Behavior Therapy (CBT) strategies for mood- and anxiety-related problems, contributing to the prevention of clinically significant depression and anxiety disorders and serving as an adjunct to therapeutic interventions delivered by trained health professionals. MoodMission was designed to deliver strategies in the form of real-time, momentary responses to user-reported low moods and anxiety. The development process involved: (a) construction of a battery of strategies, (b) empirical evaluation, (c) a software and behavioral plan design and testing process, (d) user feedback, and (e) a public launch. A pilot study of 44 participants completed the Mobile Application Rating Scale (MARS; Hides et al., 2014) for usability testing and feedback. MoodMission was rated significantly higher than standardized health app norms on the majority of the domains, including Entertainment, Interest, Customization, Target Group, Graphics, Visual Appeal, Quality of Information, Quantity of Information, Visual Information, Credibility of Source, Recommendation to Use, Estimated Frequency of Use, and Overall Rating (Hedges’s g range 0.57–1.97, p < .006). Case examples illustrate the practical uses of the app. In addition to clinical applications, MoodMission holds promise as a research tool either as an augmentation to clinician-delivered therapy, or as a vehicle for standardizing client access to specific CBT strategies (e.g., in studies intending to study different change processes).

A novel based software testing process for user-friendly projects

Technology is growing very fast with various changes in Computer Systems. They are chasing human life. They are used in various fields around us. There are some governments who fund for computer systems. Still research related them is carry on and will carry. Mostly there services are used in defense purpose. Based on computer technology there are so many improvement in them, even embedded system are developed. Any system can present some risk to its owner’s, users and environment. Risk is based on low and high. Some systems present more risk than others. Those which are related to more risk are called safety-critical systems. Safety critical systems are categorized into dangerous systems whose failures could result in loss of life, loss of revenue, knowing them and unable to solve them is sabotage to property damage or damage to the environment. This paper focus better exercises for making good user-friendly software. Every system has two sides as if coin, one side is the physical system with hardware and other side is software. If anything is neglected, there is a chance of bug appears at run-time. This may cause loss to humans. Again a committee report will be preserved for future purpose.

An optimization approach for automated unit test generation tools using multi-objective evolutionary algorithms

High code coverage is measured by the process of software testing typically using automatic test case generation tools. This standard approach is usually used for unit testing to improve software reliability. Most automated test case generation tools focused just on code coverage without considering its cost and redundancy between generated test cases. To obtain optimized high code coverage and to ensure minimum cost and redundancy a Multi-Objectives Evolutionary Algorithm approach (MOEA) is set in motion. An efficient approach is proposed and applied to different algorithms from MOEA Frame from the separate library with three fitness functions for Coverage, Cost, and Redundancy. Four MEOA algorithms have been proven reliable to reach above the 90 percent code coverage: NSGAII, Random, SMSEMOA,v and ε-MOEA. These four algorithms are the key factors behind the MOEA approach.

Feedback-based integrated prediction: Defect prediction based on feedback from software testing process

Test resource constraints is a common phenomenon in software testing. Using defect prediction to guide the resource allocation can significantly improve the efficiency and effectiveness of available test resources. However, traditional defect prediction (t-DP) is a static strategy, where the predictor cannot be dynamically adjusted during the software testing process (STP). This paper combines defect prediction with feedback control in STP and proposes a feedback-based defect prediction model, where the test results generated during STP is used as feedback information for on-line adjustment of predictor to optimize the prediction result. In addition, a novel approach called feedback-based integrated prediction (FIP) is proposed to improve the prediction accuracy, where a global predictor and a local predictor are employed to make an integrated prediction using the weight to adjust the effects of predictors at different test stages. A systematic experiment is conducted to investigate the performance of the FIP over 10 public data sets. Results show that FIP has better prediction efficiency and better robustness for external data than the t-DP, especially when the percentage of the test modules is 40%.

A Business Software Testing Process-Based Model Design

This study analyzes relevant contemporary software testing process models. In addition to contemporary theoretical models, the study also analyzes business software testing process models in a considerable number of software organizations. The dual (i.e. theoretical and empirical) analysis of the testing process aims to provide a basis for a testing process model design which is specific to testing business software in small and medium software organizations. The empirical study was conducted by a survey research strategy in 24 software organizations. In order to gather detailed information on the testing process, an interview was conducted on a purpose-selected sample of four organizations. The gathered data were processed by quantitative and qualitative data analysis. The results of theoretical and empirical research were used as a basis for attaining the study’s desired outcome: the business software testing process-based model which was graphically presented in BPMN 2.0 notation and described according to the ISO/IEC TR 24774 standard for process description in systems and software engineering. The combination of the graphic representation of the model and its description in compliance with the process description is a proven method in process management, which should enable easier understanding, and thus the implementation of the model in small and medium software organizations.

What We Know about Software Test Maturity and Test Process Improvement

In many companies, software testing practices and processes are far from mature and are usually conducted in an ad hoc fashion. Such immature practices lead to negative outcomes—for example, testing that doesn’t detect all the defects or that incurs cost and schedule overruns. To conduct test maturity assessment (TMA) and test process improvement (TPI) systematically, researchers and practitioners have proposed various approaches and frameworks. Motivated by a recent industrial project in TMA and TPI and wanting to identify the state of the art and practice in this area, researchers conducted a review of both the scientific literature and practitioners’ gray literature (for example, blog posts). The review identified 58 test maturity models and many sources with varying degrees of empirical evidence. The review’s results can serve as an evidence-based overview of and index to the vast body of knowledge in this important, fast-growing area. Using this knowledge, both researchers and practitioners should be able to assess and improve the maturity of test processes.

An optimized feature selection using fuzzy mutual information based ant colony optimization for software defect prediction

In recent years, there is a significant notification focused towards the prediction of software defect in the field of software engineering. The prediction of software defects assist in reducing the cost of testing effort, improving the process of software testing and to concentrate only on the fault-prone software modules. Recently, software defect prediction is an important research topic in the software engineering field. One of the important factors which effect the software defect detection is the presence of noisy features in the dataset. The objective of this proposed work is to contribute an optimization technique for the selection of potential features to improve the prediction capability of software defects more accurately. The Fuzzy Mutual Information Ant Colony Optimization is used for searching the optimal feature set with the ability of Meta heuristic search. This proposed feature selection efficiency is evaluated using the datasets from NASA metric data repository. Simulation results have indicated that the proposed method makes an impressive enhancement in the prediction of routine for three different classifiers used in this work.

A phase wise approach for fault identification

Making software with high quality assurance is becoming difficult due to software testing and maintenance is costly and time consuming phases of software development life cycle (SDLC). This takes more than half the cost of software development. Researchers are already proposed methods to reduce the cost and time of software testing. However, these methods still not properly handle software testing process. The assurance of software quality is very crucial for any organizations so the cost of testing is increasing day by day. Therefore the scope of finding the fault from software is challenging task. However the evaluation of software quality at early stage is related to human centric and expert knowledge about the software. For this a phase wise fault identification approach using fuzzy inference system has proposed. This method finds the fault in every phase of the software based on fuzzy intuitive based approach. The fuzzy inference systems have applied with every phase to identify the fault. The resultant fault is used for the analysis of the software. This will provide sufficient time for developers, designers and tester to handle the fault in a given time frame. The work will help to increase the software quality.

Augmenting SCA project management and automation framework

In our daily life we need to keep the records of the things in order to manage it in more efficient and proper way. Our Company manufactures semiconductor chips and sale it to the buyer. Sometimes it manufactures the entire product and sometimes partially and sometimes it sales the intermediary product obtained during manufacturing, so for the better management of the entire process there is a need to keep the track record of all the entity involved in it. Materials and Methods: Therefore to overcome with the problem the need raised to develop the framework for the maintenance of the project and for the automation testing. Project management framework provides an architecture which supports in managing the project by marinating the records of entire requirements, the test cases that were created for testing each unit of the software, defect raised from the past years. So through this the quality of the project can be maintained. Results: Automation framework provides the architecture which supports the development and implementation of the automation test script for the software testing process. Conclusion: For implementing project management framework the product of HP that is Application Lifecycle management is used which provides central repository to maintain the project.

Software reliability prediction using a deep learning model based on the RNN encoder–decoder

Different software reliability models, such as parameter and non-parameter models, have been developed in the past four decades to assess software reliability in the software testing process. Although these models can effectively assess software reliability in certain testing scenarios, no single model can accurately predict the fault number in software in all testing conditions. In particular, modern software is developed with more sizes and functions, and assessing software reliability is a remarkably difficult task. The recently developed deep learning model, called deep neural network (NN) model, has suitable prediction performance. This deep learning model not only deepens the layer levels but can also adapt to capture the training characteristics. A comprehensive, in-depth study and feature excavation ultimately shows the model can have suitable prediction performance. This study utilizes a deep learning model based on the recurrent NN (RNN) encoder–decoder to predict the number of faults in software and assess software reliability. Experimental results show that the proposed model has better prediction performance compared with other parameter and NN models.

Comparative Evaluation of the State-of-art Requirements-based Test Case Generation Approaches

The overall aim of software testing is to deliver the error-free and high-quality software products to the end users. The testing process ensures that a software is aligned with the user specification and requirements. In software testing process, there are many challenging tasks however test case generation process is considered as the most challenging one. The quality of the generated test cases has a significant impact on efficiency and effectiveness of the testing process. In order to improve the quality of a developed software, the test cases should be able to achieve maximum adequacy in the testing and requirements' coverage. This paper presents a comparative evaluation of the prominent requirement-based test case generation approaches. Five evaluation criteria namely, inputs for test case generation, transformation techniques, coverage criteria, time and tool's support are defined to systematically compare the approaches. The results of the evaluation are used to identify the gap in the current approaches and research opportunities in requirement-based test case's generation.

Probability Model-Based Test Suite Reduction

The paper presents a test suite reduction approach based on probability models in regression testing. First, we model the process of software testing by using the joint probability of both test cases and program faults, and propose two probability models for the software's old and new versions, respectively. Then, a theorem combining set operations and probability models is given to implement our approach. Different from the traditional coverage-based test suite reduction methods, the reduced test suite constructed by our approach does not need to cover all test requirements, but also remains the same fault detection capability of the original test suite.The paper presents a test suite reduction approach based on probability models in regression testing. First, we model the process of software testing by using the joint probability of both test cases and program faults, and propose two probability models for the software's old and new versions, respectively. Then, a theorem combining set operations and probability models is given to implement our approach. Different from the traditional coverage-based test suite reduction methods, the reduced test suite constructed by our approach does not need to cover all test requirements, but also remains the same fault detection capability of the original test suite.

BWDM: Test Cases Automatic Generation Tool Based on Boundary Value Analysis with VDM++

For software development using Formal Methods, we have developed a prototype of the boundary value test case automatic generation tool BWDM. The main two topics of our tool are (1) automatically generation of test cases and (2) boundary value analysis. Our tool improves the efficiency of software testing process in using VDM++ that is one of the Formal Methods. In this research, we show the composition of our tool, application example, evaluation of the usefulness, relative research, and future issues.

The Impact of Human Factors on the Software Testing Process: The Importance of These Factors in a Software Testing Environment

Software testing is a key process that ensures a reliable quality product, and like other activities in the development process, have a wide range of tools available, but still requires a lot of human work, where the final quality of the software can be impacted directly by several factors. In this sense, this study aims to identify the human factors (cognitive, operational and organizational) present in the test process and to define the influence of these factors during their execution. Thus, the article presents a study with quantitative methods and techniques of the survey type, in which 112 professionals from the test area participated in 17 Brazilian states. The results provide a set of human factors that correlate with the final quality of a software product or service.

A replicated empirical study to evaluate software testing methods

Many empirical studies have been performed to evaluate software testing methods in past decades. However, we are still not able to generalize the results as most studies are not complete and differ significantly from one another. To contribute to the existing knowledge base of software testing methods, we performed an empirical study to evaluate 3 testing methods: (1) code reading by stepwise abstraction, (2) functional testing using equivalence partitioning and boundary value analysis, and (3) structural testing using 100% branch, multiple-condition, loop, and relational-operator coverage using a well-defined and standard schema. A controlled experiment is performed with 18 subjects who applied the 3 defect detection techniques to 3 C programs in a fractional factorial experimental design to observe failures and isolate faults. The experimental results show that (1) the techniques are equally effective in terms of observing failures and finding faults, (2) the effectiveness of the techniques depends on the nature of the program, (3) all the testing techniques are equally efficient in case of failure observation, (4) the techniques are different in their efficiency in terms of fault isolation where code reading performed better than that of structural and functional testing, and (5) with respect to the fault types, all the techniques were equally effective in observing failures and isolating faults except in case of cosmetic faults where functional testing performed better than the other 2 techniques The effectiveness and efficiency of testing techniques were significantly influenced by the type of program. The results presented in this paper contribute to an empirical knowledge base of testing methods and may be helpful for the software engineers to decide the appropriate techniques in improving the software testing process.

Evaluation of Software Testing Techniques Using Artificial Neural Network

Software Industry plays a vital role in the current environment, it is very essential to minimize the fault in the existing software products. In SDLC life cycle, software testing becomes very much important which finds faults in the software that increases the efficiency of software. Most of the cost is occupied by the software testing process, it is product very essential to implement the automation technique that reduces the cost that increases the software reliability. In the automated testing process, various intelligent methods are used in order to avoid the manual process. Automation testing tools can be implemented to increase the quality of the software.

Generation of Test Cases for Object Oriented Software using UML State Machine Diagram

Software testing is one of the most important phases of software development life cycle. Software testing is being used by all the software developers to test the overall working of their software. Testing is one of the most important means to validate the correctness of systems. A UML state machine diagram is a model which represents the dynamic behaviour of the system. The aim of this paper is to fully automate the testing process of software by generating the test cases in the design phase where, the state machine diagram is modelled using standard Unified Modelling Language (UML) tool called StarUML which exports the state machine diagram to its internal XMI representation. This XMI file is then pre-processed to extract the relevant data and store the data into an immediate data structure. After traversing the data structure, a set of test path will be generated and for each test path, test case will be generated