Software Quality Engineering Research Articles

Principles of Creating Multi-objective Quality Models for Software Systems

This article proposes an original approach at a mathematical level to the creation of multi- objective quality models for software systems. The research is based on the study and generalization of quality modeling trends of software systems and user needs in order to determine optimal principles for constructing such models. The article provides mathematical explanations that play a key role in identifying and formalizing the principles of creating multi-objective quality models of software. An important aspect is the consideration of quality model construction principles at the mathematical level, allowing for a more precise assessment and analysis of various aspects of software quality. The research results indicate that incorporating mathematical principles into the creation of multi-objective quality models for software systems can have a significant practical impact. It is established that on a practical level of developing multi-objective quality models for software systems, consideration of the principles of creating multi-objective quality models can have numerous practical implications. Specifically, the application of metrics within established principles allows for a comprehensive view of software quality and identifies areas requiring attention and improvement. This helps developers and software quality engineers make informed decisions regarding system improvement and optimization. The research has shown that creating high-quality models of quality requires attention to various aspects, from user needs to testing and continuous improvement, as well as the use of mathematical methods for their formalization and analysis. The developed principles of creating multi-objective quality models at the level of mathematical models allow the use of these models to assess and analyze various aspects of software quality, representing each model using a corresponding function that determines the relationship between quality metrics and the quality of the software itself. It is expected that further development and implementation of these principles will contribute to improving software development processes and ensure high quality of the resulting software.

Talent Cultivation Quality of Software Engineering Majors Based on Deep Learning

Online learning, to cultivate talents it is inevitable to encounter some pictures or videos with poor visual quality. Deep-learning algorithms are both data-hungry and expensive to compute. These algorithms work better after being trained on a broad and extensive collection of samples. The current moment deep learning methods must urgently make use of human intellect to address the issue in a way that reduces the most expensive effort computationally. This paper analyzes the current situation of software engineering talent cultivation quality of software engineering to enhance the quality of the education is improved by Hierarchically Gated Recurrent Neural Network (HGRNN). The aim of the work is to foster the development of world-class software engineering talents. Initially, the input data’s are gathered from public dataset train 400 with 400 grey pictures. HGRNN is image de-noising module, as for the smart teaching platform to assist instructors in obtaining teaching photography with high quality and improve teaching quality. The proposed model is implemented in MATLAB/ Simulink platform and the accuracy is compared to various existing approaches such Back Propagation Network (BPN), Artificial Neural Network (ANN) and Decision Tree Algorithm (DTA) our proposed method obtains 98% of accuracy.

Improved Multi-objective Particle Swarm Optimization in Software Engineering Supervision

In the 21st century, the software industry has achieved great development. The development complexity and volume of software projects are also continuously increasing. The design of software engineering supervision network plans is becoming increasingly important. In response to the poor optimization performance and poor convergence and distribution of optimal solutions in existing network planning algorithms, the Pareto optimal solution set construction method, global extremum selection method, and fitness value determination method of multi-objective particle swarm optimization algorithm are improved to enhance the convergence and distribution of the algorithm. Traditional methods only optimize one or two objectives of network planning, resulting in inconsistency with actual engineering. A multi-objective model based on resources, duration, cost, and quality is established for comprehensive optimization. Based on the results, the Pareto optimal solution curves obtained by the proposed algorithm on three classic test functions are consistent with the actual theoretical Pareto frontier curves. The proposed method is applied to engineering project examples. 10 solutions that meet the schedule requirements are obtained. Most engineering projects have a quality of over 80%, which verifies the practicality of the algorithm. The algorithm has achieved good results in optimizing engineering quality. Therefore, this model has the ability to consider various indicators such as resources and costs to obtain software engineering quality improvement plans. It has certain application potential.

A Survey Paper Review on Advancements in AI Driven User Interface Testing

Abstract: This survey explores the quality of software engineering and highlights the important role of artificial intelligence (AI) in improving software testing. It emphasizes the importance of software testing to determine the effectiveness and capabilities of software programs. This paper highlights inconsistencies in measurement guidance and the need for automation. It will also provide a better look at the changing ecosystem of automation products driven by roles in the convergence of the artificial intelligence and machine learning (ML) eras. An AI-powered machine is made based on machine learning principles and is known as a tool that performs test models, provides logic, solves problems and performs tasks correctly. The main purpose of this evaluation is to explain the practical use of artificial intelligence in software testing and to conduct an in-depth analysis of its impact on software performance and development, improving agility. In summary, this communication provides a vision for the future by demonstrating the effectiveness of intelligent automation tools in the software testing environment, making the transition to software development reliable and convenient. More generally, this survey paper discusses today's practices of using AI to improve software development and continually unlock problem-solving innovation in software testing and software engineering along with making UI testing more reliable.

Teaching Reform Measures For Software Engineering Specialization In The Context Of New Engineering

With the rapid development of science and technology and the deepening of industrial change, the new engineering discipline has become an important direction of higher education reform. As an important part of the new engineering, the reform of software engineering education mode is of great significance for the cultivation of software engineers who can adapt to the future development. This paper firstly introduces the characteristics of software engineering under the background of new engineering discipline, then deeply analyzes the problems existing in the current teaching of software engineering, and puts forward the targeted reform measures. By practicing these reform measures, we can effectively improve the teaching quality of software engineering and cultivate more excellent software engineers with innovative ability and practical ability.

A Comprehensive Exploration of Software Quality Engineering: Principles, Practices, and Emerging Trends

A Comprehensive Exploration of Software Quality Engineering: Principles, Practices, and Emerging Trends

Advancements in Security Testing: A Comprehensive Review of Methodologies and Emerging Trends in Software Quality Engineering

Advancements in Security Testing: A Comprehensive Review of Methodologies and Emerging Trends in Software Quality Engineering

A Study on the Benefits and Limitations of Software Testing Principles and Techniques: Software Quality Engineering

A Study on the Benefits and Limitations of Software Testing Principles and Techniques: Software Quality Engineering

A Comprehensive Research Analysis of Software Development Life Cycle (SDLC) Agile & Waterfall Model Advantages, Disadvantages, and Application Suitability in Software Quality Engineering

A Comprehensive Research Analysis of Software Development Life Cycle (SDLC) Agile & Waterfall Model Advantages, Disadvantages, and Application Suitability in Software Quality Engineering

A Comprehensive Review of Performance Testing Methodologies and Best Practices: Software Quality Engineering

A Comprehensive Review of Performance Testing Methodologies and Best Practices: Software Quality Engineering

Synergizing Requirements Engineering and Quality Assurance: A Comprehensive Exploration in Software Quality Engineering

Synergizing Requirements Engineering and Quality Assurance: A Comprehensive Exploration in Software Quality Engineering

Assesing Marketing Strategy: Towards an integrated Maturity Model

Maturity models are a simple but powerful tool applied across an ample range of business disciplines. Originally emerging out of quality management and software engineering, these conceptual models help assess the quality of certain processes or strategies; and identify opportunities and actions needed to move to the next stage in maturity (Wendler, 2012). However, there is a lack of such a useful tool in the vast marketing management area of knowledge. Marketing textbooks and research are mostly focused on describing a set of definitions and proper conditions for marketing decisions, rather than having discussions on the way managers should make marketing decisions to improve the impact of those strategies (Wierenga, 2011). This needs to change. Managers who are properly guided in the marketing strategy formulation process are benefitted by minimizing intended-realized marketing strategy gaps. They also tend to maximize the ability of the marketing strategy to achieve business goals and increase the quality of that business process. The proposed theory intends to answer two key questions: How could managers systematically improve their ability to assess the maturity of their current marketing strategy formulation process? How to develop marketing plans that, because of their content, result in an increased ability to be implemented and a potentially higher success rate? To answer these questions, I propose an integrated 5-stage Maturity Model that will help managers identify the maturity of their current marketing strategy, and determine proper areas for improvement, either in the formulation process or its content.

DD-KARB: data-driven compliance to quality by rule based benchmarking

The problem of compliance checking and assessment is to ensure that the design or implementation of a system meets some desired properties and complies with some rules or regularities. This problem is a key issue in several human and engineering application domains such as organizational management and e-governance, software and IT industries, and software and systems quality engineering. To deal with this problem, some different approaches and methods have been proposed. In addition to the approaches such as formal methods, mathematical proofs, and logical evaluations, benchmarking can be used for compliance assessment. Naturally, a set of benchmarks can shape an applied solution to compliance assessment. In this paper we propose KARB solution system, i.e. keeping away compliance Anomalies through Rule-based Benchmarking. In fact, in our proposed method the rule-based benchmarking means evaluating the conformity of an under-compliance system to a set of rules. In this solution approach, the under-compliance system is specified symbolically (using formal and logical descriptions). Also, the desired rules are specified formally as the semantic logic in the evaluation process. After reviewing the proposed method, a case study was conducted to demonstrate and analyze the KARB solution. The IR-QUMA study (Iranian Survey on Quality in Messenger Apps) was then conducted to evaluate the quality of some messenger applications. According to the evaluation results, the hybrid DD-KARB method (with a combination of semantics-awareness and data-drivenness) is more effective than solo methods and can compute a good estimation for the messenger application user quality scores. Therefore, DD-KARB can be considered as a method for quality benchmarking in this technical context.

How Scrum adds value to achieving software quality?

Scrum remains the most popular agile software development method implementation for a variety of reasons; one important motive is to improve software quality. Yet many organizations fail to achieve quality improvements through the use of Scrum, and existing research sheds little light on the value-add of Scrum for software quality. More specifically, (1) how notions of software quality among Scrum practitioners relate to established quality perspectives, (2) how Scrum helps teams to achieve higher software quality and (3) why some teams fail to meet the objective of higher quality. We addressed these gaps through a two-phased qualitative study based on 39 interviews and two in-depth case studies. We find that Scrum practitioners emphasize established notions of external quality comprising of conformity to business needs and absence of defects, while they also value internal quality, especially sustainable software design. Our results show that Scrum helps teams achieve both dimensions of quality by promoting some social antecedents (collaboration, psychological safety, accountability, transparency) and process-induced advantages (iterative development, formal inspection, and adaptation). Our findings unveil how these factors contribute to achieving software quality and under what conditions their effects can fail to materialize. These conditions include inconsistent Scrum implementations, cultural constraints, team tensions, and inaccessibility of end-users. In addition, the complexity of the project aggravates the impact of these conditions. Taken together, these findings show that Scrum can complement established quality assurance and software engineering practices by promoting a social environment that is conducive to creating high-quality software. Based on our findings, we provide specific recommendations for how practitioners can create such an environment.

Determining a core view of research quality in empirical software engineering

Context:Research quality is intended to appraise the design and reporting of studies. It comprises a set of standards such as methodological rigor, practical relevance, and conformance to ethical standards. Depending on the perspective, different views of importance are given to the standards for research quality. Objective:To investigate the suitability of a conceptual model of research quality to Software Engineering (SE), from the perspective of researchers engaged in Empirical Software Engineering (ESE) research, in order to understand the core value of research quality. Method:We conducted a mixed-methods approach with two distinct group perspectives: (i) a research group; and (ii) the empirical SE research community. Our data collection approach comprised a questionnaire survey and a complementary focus group. We carried out a hierarchical voting prioritization to collect relative values for importance of standards for research quality. Results:In the context of this research, ‘internally valid’, ‘relevant research idea’, and ‘applicable results’ are perceived as the core standards for research quality in empirical SE. The alignment at the research group level was higher compared to that at the community level. Conclusion:The conceptual model was seen to express fairly the standards for research quality in the SE context. It presented limitations regarding its structure and components’ description, which resulted in an updated model.

A Software Fault Prediction on Inter- and Intra-Release Prediction Scenarios

Software quality engineering applied numerous techniques for assuring the quality of software, namely testing, verification, validation, fault tolerance, and fault prediction of the software. The machine learning techniques facilitate the identification of software modules as faulty or non-faulty. In most of the research, these approaches predict the fault-prone module in the same release of the software. The model is found to be more efficient and validated when training and tested data are taken from previous and subsequent releases of the software respectively. The contribution of this paper is to predict the faults in two scenarios (i.e., inter- and intra-release prediction). The comparison of both intra- and inter-release fault prediction by computing various performance matrices using machine learning methods shows that intra-release prediction has better accuracy compared to inter-releases prediction across all the releases. Also, both the scenarios achieve good results in comparison to existing research work.

An ontology knowledge inspection methodology for quality assessment and continuous improvement

Ontology-learning methods were introduced in the knowledge engineering area to automatically build ontologies from natural language texts related to a domain. Despite the initial appeal of these methods, automatically generated ontologies may have errors, inconsistencies, and a poor design quality, all of which must be manually fixed, in order to maintain the validity and usefulness of automated output. In this work, we propose a methodology to assess ontologies quality (quantitatively and graphically) and to fix ontology inconsistencies minimizing design defects. The proposed methodology is based on the Deming cycle and is grounded on quality standards that proved effective in the software engineering domain and present high potential to be extended to knowledge engineering quality management. This paper demonstrates that software engineering quality assessment approaches and techniques can be successfully extended and applied to the ontology-fixing and quality improvement problem. The proposed methodology was validated in a testing ontology, by ontology design quality comparison between a manually created and automatically generated ontology.

AI System Engineering—Key Challenges and Lessons Learned

The main challenges are discussed together with the lessons learned from past and ongoing research along the development cycle of machine learning systems. This will be done by taking into account intrinsic conditions of nowadays deep learning models, data and software quality issues and human-centered artificial intelligence (AI) postulates, including confidentiality and ethical aspects. The analysis outlines a fundamental theory-practice gap which superimposes the challenges of AI system engineering at the level of data quality assurance, model building, software engineering and deployment. The aim of this paper is to pinpoint research topics to explore approaches to address these challenges.

Hybrid Fuzzy Knowledge Based Prediction Model for the Software Development and Maintenance Quality in Software Engineering Approach

The main arena of Software Engineering development with ood design, development, coding, testing, implementation, deployment of the software, finally maintaining the software with good functionality. For the development of software many organizations are investing more and more budget in their revenue. Software Engineering development has several categories of data presented in software engineering such as Graphical User Interface, Usage graphs, writing text, realities and images. Significant information be able to be obtained from this composite data by well recognized data mining techniques such as association, classification, clustering etc. By discovery hidden patterns by data mining software engineering data is made illegal. Software Engineering development has many objectives in software engineering such as Code and Design optimization, Project documentation, Development cost estimation etc. Variety of significant data mining method in each phase of software development life cycle supports in realizing these objectives proficiently and the failure rate of software is decreased. . This paper focused a new hybrid model like combination of Fuzzy Logic and knowledge management offers a significant method for developing models for software quality prediction. This research paper explains about exercise of estimate and valuation at a particular organization by developments and represents the outcomes attained with a fuzzy based classification and knowledge model for the fuzzy knowledge management predication for the quality of software engineering Approach. This result illustrate that the significance of Average Error Evaluation Efficiency observed and used in fuzzy logic is lesser than Average Error Evaluation Efficiency used in another regression multiple regression; while the value of prediction is higher value that other prediction models is used before. Thus Results demonstrate that Hybrid fuzzy knowledge management predication for the quality of software engineering can be used as alternative for predicting the Software Development and Maintenance Quality (SDMQ).

IMPORTANCE OF SOFTWARE QUALITY MODELS IN SOFTWARE ENGINEERING

The purpose of this paper is to identify the importance quality in software engineering when the projects or products are developed. The degree to which a component, system or process meets specified requirements and/or user/customer needs and expectations is the quality. The totality of functionality and features of a software product that bear on its ability to satisfy stated or implied needs is software quality. Some even say that ‘quality’ cannot be defined and some say that it can be defined but only in a particular context. Some even state confidently that ‘quality is lack of bugs’. In this paper we discuss about the quality and the quality models.