A Goal-Driven Measurement Model for Software Testing Process

Software release is highly dependent upon the quality of the product which must be met by the product before software release. Software product quality outcome will be good if modeling of test process is good. In Software test process model large numbers of state variables exist which can affect the entire software testing process. This paper introduces various state variables for improving the software test process. By introducing as many state variables, definitely we can enhance the overall quality of testing process which ultimately will enhance the quality of software product. This paper proposes an algorithm focus upon the use of the state variables along with the control based system for the software testing process. Combine concept of controls based system and state variables provide the power of identifying the remaining number of errors at any point of time. Proposed algorithm uses the concept of the feedback system and related work to [9] [10] [12] [13] with additional state variables to model an efficient software testing process.

Software has become a driving force. Its impact on our society continues to be profound. Customers have come to rely on the functionality and accuracy of software. A bug free software is an illusion and infinite testing is simply not a viable option.. But with scientific software development technology, quality control and systematic testing, number of bugs could be minimized. Software testing is an important activity to improve software quality. However, it is well known that it is costly (Yang et al, 2008; Bertolino, 2008). Thus, there has always been a need to increase the efficiency of testing while, in parallel, making it more effective in terms of finding & removing defects. Measuring critical attributes of software testing process enables software developers with further insight in to software testing process. Thereafter, optimal utilization of their resources for testing purpose. In this paper, we propose a two way assessment framework for software developers to find out overall utility from the testing process that can serve as an important indicator of the efficiency and effectiveness of software testing process. It illustrates a two way assessment technique, which can be adopted to inculcate a culture of continuous improvement in the overall software testing lifecycle. The framework takes into consideration 1). Managements/Developers perspective to highlight the importance of various attributes of testing; & 2). Testers/Team Members perspective, to assess functioning and working of each of the attribute

In the software testing process, many factors can affected the failure distribution, such as the testing strategy, running environment and resource allocation, etc. Once these factors are transformed during testing phase, they could result in the failure intensity function increasing or decreasing non-monotonically and abrupt fluctuations at a certain time interval. Especially, in large software systems, detected complex faults and failures are more difficult to isolate, reproduce and remove in comparison of detected simple faults and failures. Therefore, the fault detection rate shows the irregular fluctuation over time in the software testing process. In this paper, we propose an imperfect software debugging model derived from an Ito -type stochastic differential equation (SDE). In the actual testing process, the cumulative number of fault detection can be considered a stochastic event. Thus, we can model fault detection process using SDE. We estimate the parameters of the proposed model using maximum likelihood estimation. We also use five historical fault data sets to test the performance of the proposed model. To show that the proposed model is the best model among all models, we use a new optimal method to select the best model from various software reliability models. Experimental results show that the proposed model has better fitting and predictive performance than non-homogeneous Poisson process models and other perfect software reliability models based on SDE.

Background: The development of technology, both hardware and software, is rapidly advancing due to the growth of Artificial Intelligence (AI), which simplifies many human activities, including the software testing process. This research journal discusses how software testing is conducted through a Quality Management System (QMS). Objective: The journal examines whether software testing processes are related to QMS, how significantly QMS supports software testing, and the differences between testing with QMS and without QMS. Methods: This study outlines the stages of software testing through QMS, such as Software Quality Assurance (SQA), Quality Control (QC), Test Maturity Model Integration (TMMI), and performance indicators. According to the American Society for Quality (ASQ), QMS is a formal and documented system consisting of policies, processes, and procedures designed to achieve an organization's quality policies and objectives. Results: Several experts also define QMS as a system that focuses on improving customer satisfaction, process consistency, efficiency, and the overall quality of products or services. Software, according to Roger S. Pressman, is a set of instructions (programs), data structures that allow information manipulation, and related documentation describing operations and functions. Conclusion: Overall, QMS not only improves software product quality but also strengthens the testing process, making it more optimal, efficient, and aligned with organizational goals. The integration of QMS frameworks such as ISO 9001:2015, CMMI, and TMMI provides organizations with a competitive advantage by ensuring consistent delivery of high-quality software products that meet or exceed customer expectations.

Here are obstacles in the process of making software at PT. Hartono Technology Palace. At that time, every time a test was carried out on the software being developed, the company would find different results. This causes ineffective and inefficient software development processes. Problems at PT. Hartono Istana Teknologi was found because of the less use of standard software testing methods. That is why the test results are constantly changing, and the software development process cannot be completed quickly. This problem can be solved by applying the software testing method. This research will try to solve the problem by using 2 methods, trial and error, and integration. These two methods will be compared to determine which method is more effective in the software testing process at PT. Hartono Technology Palace. Several test scenarios are also designed to be applied in this study. As a final result, the trial and error method is the most effective method to be applied at PT. Hartono Istana Teknologi. The trial and error method is considered effective because the testing process is more thorough and in accordance with the established concept. While in the integration method, the testing process only focuses on testing data without paying attention to existing functions

Software defects play a key role in software reliability, and the number of remaining defects is one of most important software reliability indexes. Observing the trend of the number of remaining defects during the testing process can provide very useful information on the software reliability. However, the number of remaining defects is not known and has to be estimated. Therefore, it is important to study the trend of the remaining software defect estimation (RSDE). In this paper, the concept of RSDE curves is proposed. An RSDE curve describes the dynamic behavior of RSDE as software testing proceeds. Generally, RSDE changes over time and displays two typical patterns: 1) single mode and 2) multiple modes. This behavior is due to the different characteristics of the testing process, i.e., testing under a single testing profile or multiple testing profiles with various change points. By studying the trend of the estimated number of remaining software defects, RSDE curves can provide further insights into the software testing process. In particular, in this paper, the Goel-Okumoto model is used to estimate this number on actual software failure data, and some properties of RSDE are derived. In addition, we discuss some theoretical and application issues of the RSDE curves. The concept of the proposed RSDE curves is independent of the selected model. The methods and development discussed in this paper can be applied to any valid estimation model to develop and study its corresponding RSDE curve. Finally, we discuss several possible areas for future research.

Software testing process is an important factor affecting the quality of software testing, which plays an important role in the entire software development life cycle. In order to achieve a precise result of testing, different software testing process model will be chosen in different software development process for testing. Traditional testing process model will be chosen in traditional software development, and agile testing process model will be chosen in agile software development. However, there is no kind of testing process model proposed for a special software development which is a combination of traditional software development and agile software development. This article will propose a hybrid software testing process model aiming at a combination of traditional software development model and agile software development model. In the following sections, traditional testing process model and agile testing process model will be introduced respectively in the first place. Most attentions will be paid to the introduction and analysis of the hybrid software testing process model through one case. Furthermore, we will discuss the relationship of the data tracing involved in the testing process model.

Software testing is an extremely important work link in software engineering. It runs through the entire life cycle of software engineering to ensure that software development meets requirements and can meet certain software quality requirements. With the continuous expansion of the scale of information systems, simple software testing can no longer meet the requirements of system and software quality assurance. This requires practitioners to gradually begin to think about and improve the testing methods of large-scale information software. For large-scale information software, testing will be embedded in each unit, and the software testing process will be organized in a more macroscopic way of thinking and organization. This article will summarize a series of methods suitable for large-scale information software testing through the research of software testing and the practice of the author. It is hoped that the research in this article can provide some help for the testing of large-scale information software, and also provide help for the precipitation and improvement of the author's own work.

Today, the most vital step is software testing, especially in product quality. Agile software development is exceedingly difficult and has contentious issues in literature and business. Since most Agile techniques place more emphasis on development than software testing activities, different people have varying opinions about how software testing fits into these methodologies. Agile places a high emphasis on frequent deliveries and tight client connections. However, software testing is complex because agile does not incorporate many destructive testing techniques typically required for a product of higher-quality. The study reveals industry-wide Agile testing procedures and the main problems that arise when Agile development is used. Additionally covered are the differences between manual and automated testing, best automation practices, and ways to lead different testing teams in an agile testing environment. This study covers every facet of the Agile software testing process. Agile development has shown that testing is not a distinct stage but rather a crucial component of software development along with the coding. The agile team employs an overall strategy to produce software products of the highest quality. As part of an agile team, testers collect customer samples of the desired behaviour and collaborate with the developer's team to help the coders translate these examples into executable specifications. Coding and testing are done iteratively and incrementally, developing every item until it is valuable enough to release in production. It addresses several testing modalities. The classification provided by its quadrants aids teams in determining and organizing the necessary tests. To increase quality assurance, this article will examine various and improved Agile Testing Processes and methodologies. Additionally, the topic of Agile development's software testing method is covered. Agile development processes can be improved and more advantageous by incorporating testing procedures.

Software Testing plays an important role in making high quality products and the right time. The process of testing done manually is often inaccurate, unreliable, and needed more than automatic testing. This research proposes a new framework for automation testing. This framework will help developers to create applications with better quality and shorten testing time. This framework offers a solution for developers so that the testing process is carried out easily and quickly. Our proposed concept consists of an automated test script based on Serenity Framework and can be done as a background process using Jenkins. Input of the system is a testing scenario, then mapped into Java Programming Language. Output of this system are test reports that represent the scenario that has been carried out. the results of implementation system prove that developers are helped by this framework in the software testing process. So that in this study it can be concluded that the automated testing framework that has been developed can improve the quality of application products through effective and efficient work methods.

Software reliability testing refers to various software testing activities that are driven to achieve a quantitative reliability goal given a priori or lead to a quantitative reliability assessment for the software under test. In this paper we develop a modeling framework for the software reliability testing process, comprising a simplifying model and a generalized model. In both models the software testing action selection process and the defect removal mechanism are explicitly described. Both the discrete-time domain and the continuous-time domain are involved. The generalized model is more accurate or realistic than the simplifying model since the former avoids the assumption that defects are equally detectable and the assumption that defects are removed upon being detected. However simulation examples show that the simplifying model really captures some of essential features of the software testing process after a short initial testing stage. The modeling framework is practically realistic, mathematically rigorous, and quantitatively precise. It demonstrates that the relationship between software testing and delivered software reliability, which was poor understood, can well be formulated and quantified. Rigorous examinations show that several common assumptions adopted in software reliability modeling, including the independence assumption, the exponentiality assumption, and the NHPP assumption, are theoretically false in general. This paper sets a good starting point to further formalize and quantify the software testing process and its relation to delivered software reliability.

Software testing forms an integral part of the software development life cycle. Since the objective of testing is to ensure the conformity of an application to its specification, a test “oracle” is needed to determine whether a given test case exposes a fault or not. Using an automated oracle to support the activities of human testers can reduce the actual cost of the testing process and the related maintenance costs. In this paper, we present a new concept of using an artificial neural network as an automated oracle for a tested software system. A neural network is trained by the backpropagation algorithm on a set of test cases applied to the original version of the system. The network training is based on the “black-box” approach, since only inputs and outputs of the system are presented to the algorithm. The trained network can be used as an artificial oracle for evaluating the correctness of the output produced by new and possibly faulty versions of the software. We present experimental results of using a two-layer neural network to detect faults within mutated code of a small credit approval application. The results appear to be promising for a wide range of injected faults. ? 2002 John Wiley & Sons, Inc.

In the software testing process, the nature of the failure data is affected by many factors, such as the testing environment, testing strategy, and resource allocation. These factors are unlikely to all be kept stable during the entire process of software testing. As a result, the statistical structure of the failure data is likely to experience major changes. Recently, some useful non homogeneous Poisson process (NHPP) models with change-point are proposed. However, in many realistic situations, whether a change-point exists is unknown. Furthermore, some real data seem to have two or more change-points. In this article we propose test statistics to test the existence of change-point(s). The experimental results of real data show that our tests perform well.

The research object of our work is evaluating program for the component-based software testing. As an important part into the software engineering, software testing is the primary instrumentality to guarantee the quality and reliability of the software. With the Component-Based Software Development (CBSD) method becoming a popular method of software engineering, people pay more and more attention to the quality of component software. Therefore, the component-based software testing is an important method to guarantee the quality of component software. There are many factors existing in the component-based software testing process, in order to avoid some errors and defects and improve the testing process, we need evaluate the validity of component-based software testing. Based on the discussion of the quality characteristics of the software component, the study studies the workflow and the relevant measures which apply to evaluate the validity of component-based software testing.

In recent years, software development models have undergone changes. In order to meet user needs and functional changes, open-source software continuously improves its software quality through successive releases. Due to the iterative development process of open-source software, open-source software testing also requires continuous learning to understand the changes in the software. Therefore, the fault detection process of open-source software involves a learning process. Additionally, the complexity and uncertainty of the open-source software development process also lead to stochastically introduced faults when troubleshooting in the open-source software debugging process. Considering the phenomenon of learning factors and the random introduction of faults during the testing process of open-source software, this paper proposes a reliability modeling method for open-source software that considers learning factors and the random introduction of faults. Least square estimation and maximal likelihood estimation are used to determine the model parameters. Four fault data sets from Apache open-source software projects are used to compare the model performances. Experimental results indicate that the proposed model is superior to other models. The proposed model can accurately predict the number of remaining faults in the open-source software and be used for actual open-source software reliability evaluation.