Increase Software Quality Research Articles

Empirical Analysis of Widely Used Website Automated Testing Tools

In today's software development, achieving product quality while minimising cost and time is critical. Automated testing is crucial to attaining these goals by lowering inspection efforts and discovering faults more effectively. This paper compares widely used automated testing tools, such as Selenium, Appium, Java Unit (JUnit), Test Next Generation (TestNG), Jenkins, Cucumber, LoadRunner, Katalon Studio, Simple Object Access Protocol User Interface (SoapUI), and TestComplete, based on functionality, ease of use, platform compatibility, and integration capabilities. Our findings show that no single tool is inherently superior, with each excelling in certain areas such as online, mobile, Application Programming Interface (API), or performance testing. While Selenium and Appium are the dominant online and mobile testing frameworks, TestComplete and Katalon Studio offer complete, user-friendly cross-platform testing solutions. Despite the benefits of automation, obstacles such as tool maintenance, scalability, and cost issues remain. The report finishes with advice for picking the best tool for the project and offers potential approaches for enhancing testing frameworks, such as AI-driven optimisation, cloud-based testing, and greater Continuous Integration/ Continuous Deployment (CI/CD) integration. This study offers useful information for developers and testers looking to optimise their testing methods and increase software quality.

A machine learning approach to predict DevOps readiness and adaptation in a heterogeneous IT environment

Software and information systems have become a core competency for every business in this connected world. Any enhancement in software delivery and operations will tremendously impact businesses and society. Sustainable software development is one of the key focus areas for software organizations. The application of intelligent automation leveraging artificial intelligence and cloud computing to deliver continuous value from software is in its nascent stage across the industry and is evolving rapidly. The advent of agile methodologies with DevOps has increased software quality and accelerated its delivery. Numerous software organizations have adopted DevOps to develop and operate their software systems and improve efficiency. Software organizations try to implement DevOps activities by taking advantage of various expert services. The adoption of DevOps by software organizations is beset with multiple challenges. These issues can be overcome by understanding and structurally addressing the pain points. This paper presents the preliminary analysis of the interviews with the relevant stakeholders. Ground truths were established and applied to evaluate various machine learning algorithms to compare their accuracy and test our hypothesis. This study aims to help researchers and practitioners understand the adoption of DevOps and the contexts in which the DevOps practices are viable. The experimental results will show that machine learning can predict an organization's readiness to adopt DevOps.

Predicting software defects with swarm-intelligence-based machine learning algorithm for improved process quality

The quality and effectiveness of software systems may be significantly impacted by Software Defects (SD). Therefore, enhancing process quality is essential for controlling and minimizing the incidence of faults. Implementing reliable Software Development (SDe) processes and best practices is one way to do this information. SD, commonly referred to as software bugs or software mistakes, are defects or errors that occur in computer programs and cause them to act up or create unintended outcomes. These vulnerabilities may appear for several causes, including programming mistakes, poor design choices, or issues with the SDe cycle. The prediction of software problems based on Machine Learning (ML) using an Enhanced Artificial Neural Network (E-ANN) is implemented in this study to increase software quality and testing effectiveness. Particle swarm optimization and grey wolf optimization, these two algorithms named grey wolf swarm optimization algorithm (GWSA), are combined to recognize the corresponding compensation of the methods following the respective benefits and drawbacks. The hybrid algorithm-based model to the conventional hyperparameter optimization strategy and a single swarm intelligence algorithm's investigation of investigational consequences from six data sets shows that the hybrid algorithm-based model has high and enhanced indicators. Processed by the autoencoder, the model's performance has also improved.

Moth Flame Optimization Based FCNN for Prediction of Bugs in Software

The software engineering technique makes it possible to create high-quality software. One of the most significant qualities of good software is that it is devoid of bugs. One of the most time-consuming and costly software procedures is finding and fixing bugs. Although it is impossible to eradicate all bugs, it is feasible to reduce the number of bugs and their negative effects. To broaden the scope of bug prediction techniques and increase software quality, numerous causes of software problems must be identified, and successful bug prediction models must be implemented. This study employs a hybrid of Faster Convolution Neural Network and the Moth Flame Optimization (MFO) algorithm to forecast the number of bugs in software based on the program data itself, such as the line quantity in codes, methods characteristics, and other essential software aspects. Here, the MFO method is used to train the neural network to identify optimal weights. The proposed MFO-FCNN technique is compared with existing methods such as AdaBoost (AB), Random Forest (RF), K-Nearest Neighbour (KNN), K-Means Clustering (KMC), Support Vector Machine (SVM) and Bagging Classifier (BC) are examples of machine learning (ML) techniques. The assessment method revealed that machine learning techniques may be employed successfully and through a high level of accuracy. The obtained data revealed that the proposed strategy outperforms the traditional approach.

Prediction of Software Bugs Using FCM Fused Artificial Neural Network Classifier

The software engineering technique makes it possible to create high-quality software. One of the most significant qualities of good software is that it is bug-free. Bug detection and prevention aims to provide high-quality software that reduces the cost and time required to fix a defect, increases productivity, and allows for complete customer satisfaction. While it will be impossible to eradicate all flaws, it will be able to reduce the number of problems and their negative influence on software quality. This may be accomplished by putting in place a bug prediction mechanism that focuses on increasing software quality by lowering defect density. To extract usable information, soft computing approaches such as data mining and machine learning algorithms can be used. Soft computing approaches have been used to solve the problem of software bug prediction in a number of researches. The ability to predict necessary bugs considerably sooner in the development process is a major incentive for this study. The purpose is to stimulate people's interest in software quality testing and encourage them to employ fuzzy c means clustering (FCM), artificial neural network (ANN), and machine learning approaches to solve challenges in the field of software project success prediction. The FCM fused ANN used for bug prediction model in this study to predict software reliability. With an accuracy of 98.237percent, this research effort exhibits the usefulness and applicability of FCM fused ANN technology.

The Effect of Spray Water on an Automotive LIDAR Sensor: A Real-Time Simulation Study

This paper presents the first of its kind real-time simulation of the effect of spray water on an automotive LIDAR sensor. The simulation is based on physically measurable quantities in order to facilitate its validation and extension. Both the sensor and the environment are simulated using open source software tools: Blender and Cycles, with the objective of facilitating standardization and broader adoption. The parameters required to construct and calibrate the simulation are extracted from real measurements done on a test area and highway covering different water film thicknesses and vehicle speeds. The simulation is validated in different stages, first by comparing the trajectories of the particles with a regular solver and secondly, by the spatial distribution of the virtual and real point clouds with the measurement results. The resulting framework can be easily expanded to cover scenarios that are more complex and other phenomena counterproductive to the performance of a LIDAR sensor like dirt, exhaust gases, snow, rain and fog. The virtually generated point clouds can be used to validate the performance of the software required for the segmentation, classification and tracking of objects generated by the LIDAR sensor, thereby reducing development costs and increasing software quality.

Code Smell Detection Using Whale Optimization Algorithm

Software systems have been employed in many fields as a means to reduce human efforts; consequently, stakeholders are interested in more updates of their capabilities. Code smells arise as one of the obstacles in the software industry. They are characteristics of software source code that indicate a deeper problem in design. These smells appear not only in the design but also in software implementation. Code smells introduce bugs, affect software maintainability, and lead to higher maintenance costs. Uncovering code smells can be formulated as an optimization problem of finding the best detection rules. Although researchers have recommended different techniques to improve the accuracy of code smell detection, these methods are still unstable and need to be improved. Previous research has sought only to discover a few at a time (three or five types) and did not set rules for detecting their types. Our research improves code smell detection by applying a search-based technique; we use the Whale Optimization Algorithm as a classifier to find ideal detection rules. Applying this algorithm, the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the within-class variance. The proposed framework adopts if-then detection rules during the software development life cycle. Those rules identify the types for both medium and large projects. Experiments are conducted on five open-source software projects to discover nine smell types that mostly appear in codes. The proposed detection framework has an average of 94.24% precision and 93.4% recall. These accurate values are better than other search-based algorithms of the same field. The proposed framework improves code smell detection, which increases software quality while minimizing maintenance effort, time, and cost. Additionally, the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.

An exploratory study on confusion in code reviews

ContextCode review is a widely used technique of systematic examination of code changes which aims at increasing software quality. Code reviews provide several benefits for the project, including finding bugs, knowledge transfer, and assurance of adherence to project guidelines and coding style. However, code reviews have a major cost: they can delay the merge of the code change, and thus, impact the overall development process. This cost can be even higher if developers do not understand something, i.e., when developers face confusion during the code review.ObjectiveThis paper studies the phenomenon of confusion in code reviews. Understanding confusion is an important starting point to help reducing the cost of code reviews and enhance the effectiveness of this practice, and hence, improve the development process.MethodWe conducted two complementary studies. The first one aimed at identifying the reasons for confusion in code reviews, its impacts, and the coping strategies developers use to deal with it. Then, we surveyed developers to identify the most frequently experienced reasons for confusion, and conducted a systematic mapping study of solutions proposed for those reasons in the scientific literature.ResultsFrom the first study, we build a framework with 30 reasons for confusion, 14 impacts, and 13 coping strategies. The results of the systematic mapping study shows 38 articles addressing the most frequent reasons for confusion. From those articles, we found 13 different solutions for confusion proposed in the literature, and five impacts were established related to the most frequent reasons for confusion.ConclusionsBased on the solutions identified in the mapping study, or the lack of them, we propose an actionable guideline for developers on how to cope with confusion during code reviews; we also make several suggestions how tool builders can support code reviews. Additionally, we propose a research agenda for researchers studying code reviews.

Cybersecurity Challenges in Industry: Measuring the Challenge Solve Time to Inform Future Challenges

Cybersecurity vulnerabilities in industrial control systems have been steadily increasing over the last few years. One possible way to address this issue is through raising the awareness (through education) of software developers, with the intent to increase software quality and reduce the number of vulnerabilities. CyberSecurity Challenges (CSCs) are a novel serious game genre that aims to raise industrial software developers’ awareness of secure coding, secure coding guidelines, and secure coding best practices. An important industry-specific requirement to consider in designing these kinds of games is related to the whole event’s duration and how much time it takes to solve each challenge individually—the challenge solve time. In this work, we present two different methods to compute the challenge solve time: one method based on data collected from the CSC dashboard and another method based on a challenge heartbeat. The results obtained by both methods are presented; both methods are compared to each other, and the advantages and limitations of each method are discussed. Furthermore, we introduce the notion of a player profile, which is derived from dashboard data. Our results and contributions aim to establish a method to measure the challenge solve time, inform the design of future challenges, and improve coaching during CSC gameplay.

Software Risk Analysis with the use of Classification Techniques: A Review

Risk analysis and management is a critical aspect of the software development process. Various risks are associated with every phase of the software development lifecycle. The early identification of risks in each phase of software development coupled with mitigating plans can help to reduce the cost of the product and increase software quality. This study aims to explore various tools and techniques used in the literature of analyzing and managing risks. Most risk analysis techniques have been applied in the requirement analysis phase, so there is a scarcity of tools supporting automated risk analysis. Accommodating various types of risk factors to predict the software risks reduces the accuracy of the classifier.

Quality of Automated Program Repair on Real-World Defects

Automated program repair is a promising approach to reducing the costs of manual debugging and increasing software quality. However, recent studies have shown that automated program repair techniques can be prone to producing patches of low quality, overfitting to the set of tests provided to the repair technique, and failing to generalize to the intended specification. This paper rigorously explores this phenomenon on real-world Java programs, analyzing the effectiveness of four well-known repair techniques, GenProg, Par, SimFix, and TrpAutoRepair, on defects made by the projects’ developers during their regular development process. We find that: (1) When applied to real-world Java code, automated program repair techniques produce patches for between 10.6 and 19.0 percent of the defects, which is less frequent than when applied to C code. (2) The produced patches often overfit to the provided test suite, with only between 13.8 and 46.1 percent of the patches passing an independent set of tests. (3) Test suite size has an extremely small but significant effect on the quality of the patches, with larger test suites producing higher-quality patches, though, surprisingly, higher-coverage test suites correlate with lower-quality patches. (4) The number of tests that a buggy program fails has a small but statistically significant positive effect on the quality of the produced patches. (5) Test suite provenance, whether the test suite is written by a human or automatically generated, has a significant effect on the quality of the patches, with developer-written tests typically producing higher-quality patches. And (6) the patches exhibit insufficient diversity to improve quality through some method of combining multiple patches. We develop JaRFly, an open-source framework for implementing techniques for automatic search-based improvement of Java programs. Our study uses JaRFly to faithfully reimplement GenProg and TrpAutoRepair to work on Java code, and makes the first public release of an implementation of Par. Unlike prior work, our study carefully controls for confounding factors and produces a methodology, as well as a dataset of automatically-generated test suites, for objectively evaluating the quality of Java repair techniques on real-world defects.

The Applicability of Process-Orientation to Software Development Projects

The progress in the digital single market (DSM) has been acknowledged as one of the 10 political priorities by the European Commission since 2015. It could contribute € 415 billion per year (GDP) to the economy of the 28 EU Member States and create hundreds of thousands of new jobs. Nowadays, the ICT sector and the European Digital Agenda have declared it as one of the seven pillars of the Europe 2020 strategy. In order to speed up the development of new information technology and its commercialisation, it is necessary to increase software quality aimed at accelerating and improving technology transfer, taking into account process quality management. The aim of this article is to give an overview of a new approach to producing an additional value of the software development projects to improve the technology transfer process.

Cost Effectiveness of Software Defect Prediction in an Industrial Project

AbstractSoftware defect prediction is a promising approach aiming to increase software quality and, as a result, development pace. Unfortunately, the cost effectiveness of software defect prediction in industrial settings is not eagerly shared by the pioneering companies. In particular, this is the first attempt to investigate the cost effectiveness of using the DePress open source software measurement framework (jointly developed by Wroclaw University of Science and Technology, and Capgemini software development company) for defect prediction in commercial software projects. We explore whether defect prediction can positively impact an industrial software development project by generating profits. To meet this goal, we conducted a defect prediction and simulated potential quality assurance costs based on the best possible prediction results when using a default, non-tweaked DePress configuration, as well as the proposed Quality Assurance (QA) strategy. Results of our investigation are optimistic: we estimated that quality assurance costs can be reduced by almost 30% when the proposed approach will be used, while estimated DePress usage Return on Investment (ROI) is fully 73 (7300%), and Benefits Cost Ratio (BCR) is 74. Such promising results, being the outcome of the presented research, have caused the acceptance of continued usage of the DePress-based software defect prediction for actual industrial projects run by Volvo Group.

Neuroconductor: an R platform for medical imaging analysis.

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience.

Software hazard analysis for nuclear digital protection system by Colored Petri Net

The software safety of a nuclear digital protection system is critical for the safety of nuclear power plants as any software defect may result in severe damage. In order to ensure the safety and reliability of safety-critical digital system products and their applications, software hazard analysis is required to be performed during the lifecycle of software development. The dynamic software hazard modeling and analysis method based on Colored Petri Net is proposed and applied to the safety-critical control software of the nuclear digital protection system in this paper. The analysis results show that the proposed method can explain the complex interactions between software and hardware and identify the potential common cause failure in software properly and effectively. Moreover, the method can find the dominant software induced hazard to safety control actions, which aids in increasing software quality.

Software Library Investment Metrics: a New Approach, Issues and Recommendations

Software quality is considered as one of the most highly interacting aspects in software engineering. It has many dimensions that vary depending on the users' requirements and their points of view. Thus, the varying dimensions lead to complications in measuring and defining the software quality appropriately. The use of libraries increases software quality more than using generic programming as these libraries are prepared and tested in advance. Moreover, these libraries reduce the effort spent in the designing, testing and the maintaining processes. In this research, a new model is introduced to calculate the saved effort that results from using libraries instead of generic programming in testing, coding, and productivity processes. The proposed model consists of three metrics. These metrics are the library investment ratio, the library investment level, and program simplicity. An experimental analysis has been done onto ten software products to compare the outcomes of the model with reuse percent. The outcomes show that the model gives better results than reuse percent, because the model is deepening in the source code more than the reuse percent does. Also, the model has a better effect on the improvement of software quality and productivity, rather than reuse percent.

CASE Tool Support for Variability Management in Software Product Lines

Software product lines (SPL) aim at reducing time-to-market and increasing software quality through extensive, planned reuse of artifacts. An essential activity in SPL is variability management, i.e., defining and managing commonality and variability among member products. Due to the large scale and complexity of today's software-intensive systems, variability management has become increasingly complex to conduct. Accordingly, tool support for variability management has been gathering increasing momentum over the last few years and can be considered a key success factor for developing and maintaining SPLs. While several studies have already been conducted on variability management, none of these analyzed the available tool support in detail. In this work, we report on a survey in which we analyzed 37 existing variability management tools identified using a systematic literature review to understand the tools’ characteristics, maturity, and the challenges in the field. We conclude that while most studies on variability management tools provide a good motivation and description of the research context and challenges, they often lack empirical data to support their claims and findings. It was also found that quality attributes important for the practical use of tools such as usability, integration, scalability, and performance were out of scope for most studies.

Approach for Manufacturer Independent Automated Machine Tool Control Software Test

The rising amount and complexity of machine tool control software forces developers andvendors to increase software quality assurance measures, especially system regression tests. Thesetests are mostly conducted manually at real machines and thus error-prone, expensive and timeconsuming.This paper discusses the state of the art in machine tool control software testing andproposes a new approach for the automated test of machine tool control software. Its outstandingfeature is a manufacturer-independent test environment that makes it possible to test control softwareon different control types with minimum overhead. The approach bases on a hardware-in-the-loopsimulation system and implies a concept for a uniform access to the multitude of communicationinterfaces of today’s controls. Furthermore, the paper shows the problems that make such a tooldifficult to define and gives a concept for implementation.

Test-Driven Development in scientific software: a survey

Scientific software developers are increasingly employing various software engineering practices. Specifically, scientists are beginning to use Test-Driven Development (TDD). Even with this increasing use of TDD, the effect of TDD on scientific software development is not fully understood. To help scientific developers determine whether TDD is appropriate for their scientific projects, we surveyed scientific developers who use TDD to understand: (1) TDDs effectiveness, (2) the benefits and challenges of using TDD, and (3) the use of refactoring practices (an important part of the TDD process). Some key positive results include: (1) TDD helps scientific developers increase software quality, in particular functionality and reliability; and (2) TDD helps scientific developers reduce the number of problems in the early phase of projects. Conversely, some key challenges include: (1) TDD may not be effective for all types of scientific projects; and (2) Writing a good test is the most difficult task in TDD, particularly in a parallel computing environment. To summarize, TDD generally has a positive effect on the quality of scientific software, but it often requires a large effort investment. The results of this survey indicate the need for additional empirical evaluation of the use of TDD for the development of scientific software to help organizations make better decisions.

On the weaving process of aspect-oriented product family algebra

It is widely reported that product family engineering contributes to improving productivity, increasing software quality, reducing cost and labor needs, and decreasing the time to market. With the growing complexity of product families, more sophisticated techniques are required to develop, maintain and evolve the product families. More specifically, we focus on the problem space of product families engineering and address the problems in modeling large-scale feature models. Recent research on feature models adopts the principle of separation of concerns at the feature-modeling level. However, current modularization approaches still have limitation for handling crosscutting concerns and unanticipated changes at the feature-modeling level. We use a language called Aspect-Oriented Product Family Algebra (AO-PFA) to tackle those challenges. To fully attain the benefits of the aspect-oriented paradigm at the feature-modeling level, this paper presents the formalization of the weaving process for AO-PFA. Moreover, since the weaving process is associated with the word problem, which is in general undecidable, we prove that the weaving process of AO-PFA is convergent, leads to unambiguous weaving results, and that its rewriting system is terminating and confluent.