Quality Of Software Projects Research Articles

Ensemble-Based Machine Learning Algorithms Combined with Near Miss Method for Software Bug Prediction

Abstract Software bug prediction (SBP) involves identifying or categorizing software modules likely to contain defects, utilizing underlying system properties such as software metrics. SBP plays a crucial role in enhancing software project quality and mitigating maintenance risks. Numerous machine learning (ML) algorithms have been developed to predict software bugs. Class imbalance poses a significant challenge for these algorithms, significantly impeding their effectiveness and resulting in imbalanced false-positive and false-negative outcomes. However, limited research has been conducted to specifically tackle the issue of class imbalance in the context of SBP. This study investigates the prediction performance of a homogeneous ensemble: Bagging, boosting, and voting classifiers (VC) methods combined with the under-sampling methods to address the class imbalance problem and improve the accuracy of SBP. Two ensembles are classified as bagging ensembles: decision tree (DT) and random forest (RF); two ensembles are classified as boosting ensembles: AdaBoost (AB) and gradient boosting (GB), while the DT, RF, K-Nearest Neighbours (K-NN), and support vector machine (SVM) are considered as VC. To establish the effectiveness of the proposed models, the experiments were conducted on the available benchmark datasets, which comprise five public datasets based on both class and file-level metrics. We compared and evaluated the performance of the proposed models according to several performance measures, namely accuracy, precision, recall, f-measure, Matthew’s correlation coefficient (MCC), and the area under the receiver operating characteristic curve (AUROC). The experimental findings demonstrated that the proposed models exhibit superior efficiency in predicting software bugs on balanced datasets compared to the original datasets, with an improvement of up to 11% accuracy for the class-level metrics and 10% for the file-level metrics. The results indicate that the use of data sampling techniques had a positive impact on the prediction accuracy of the presented models. We compared our proposed method with existing SBP methods based on several standard performance measures. The comparison outcomes revealed a significant superiority of our method over the prevailing state-of-the-art SBP methods across most datasets.

Graph-Driven Exploration of Issue Handling Schemes in Software Projects

The Issue Tracking System (ITS) repositories are rich sources of software development documentation that are useful in assessing the status and quality of software projects. An original model is proposed for tracing issue handling activities and their impact on project progress. As opposed to classical data mining of software repositories, we consider fine-grained features of issues which provide a better insight into project evolution. A thorough analysis of repository contents allows us to define useful metrics for characterizing issue handling schemes. These metrics are derived from the introduced graph model and developed original data mining algorithms targeting timing, issue flow progress and project actor activity aspects. This study is associated with issue processing states and their sequences (handling paths), leading to problem resolution. The introduced taxonomy of issue processing schemes facilitates the creation of a pertinent knowledge database and the identification of both bad (anomalies) and good practices. The proposed approach is illustrated with experimental results related to a representative set of ITS project repositories. These results enhance experts’ knowledge of the project and can be used for correct decision-making actions. They reveal weak points in project development and possible directions for improvement.

Detecting and resolving feature envy through automated machine learning and move method refactoring

Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.

AI-powered peer review process

Code review is a common type of peer review in Computer Science (CS) education. It’s a peer review process that involves CS students other than the original author examining source code and is widely acknowledged as an effective method for reducing software errors and enhancing the overall quality of software projects. While code review is an essential skill for CS students, they often feel uncomfortable to share their work or to provide feedback to peers due to concerns related to coding experience, validity, reliability, bias, and fairness. An automated code review process could offer students the potential to access timely, consistent, and independent feedback about their coding artifacts. We investigated the use of generative Artificial Intelligence (genAI) to automate a peer review process to enhance CS students’ engagement with code review in an industry-based subject in the School of Computing and Information System, University of Melbourne. Moreover, we evaluated the effectiveness of genAI at performing checklist-based assessments of code. A total of 80 CS students performed over 36 reviews in two different weeks. We found our genAI-powered reviewing process significantly increased students’ engagement in code review and, could also identify a larger number of code issues in short times, leading to more fixes. These results suggest that our approach could be successfully used in code reviews, potentially helping to address issues related to peer review in higher education settings.

A novel approach for software defect prediction using CNN and GRU based on SMOTE Tomek method

Software defect prediction (SDP) plays a vital role in enhancing the quality of software projects and reducing maintenance-based risks through the ability to detect defective software components. SDP refers to using historical defect data to construct a relationship between software metrics and defects via diverse methodologies. Several prediction models, such as machine learning (ML) and deep learning (DL), have been developed and adopted to recognize software module defects, and many methodologies and frameworks have been presented. Class imbalance is one of the most challenging problems these models face in binary classification. However, When the distribution of classes is imbalanced, the accuracy may be high, but the models cannot recognize data instances in the minority class, leading to weak classifications. So far, little research has been done in the previous studies that address the problem of class imbalance in SDP. In this study, the data sampling method is introduced to address the class imbalance problem and improve the performance of ML models in SDP. The proposed approach is based on a convolutional neural network (CNN) and gated recurrent unit (GRU) combined with a synthetic minority oversampling technique plus the Tomek link (SMOTE Tomek) to predict software defects. To establish the efficiency of the proposed models, the experiments have been conducted on benchmark datasets obtained from the PROMISE repository. The experimental results have been compared and evaluated in terms of accuracy, precision, recall, F-measure, Matthew’s correlation coefficient (MCC), the area under the ROC curve (AUC), the area under the precision-recall curve (AUCPR), and mean square error (MSE). The experimental results showed that the proposed models predict the software defects more effectively on the balanced datasets than the original datasets, with an improvement of up to 19% for the CNN model and 24% for the GRU model in terms of AUC. We compared our proposed approach with existing SDP approaches based on several standard performance measures. The comparison results demonstrated that the proposed approach significantly outperforms existing state-of-the-art SDP approaches on most datasets.

Factors Impacting Defect Density in Software Development Projects

This study empirically examines the impact of technical and situational factors on the quality of software development. Defect density was used to measure the post-implementation quality of software projects. The non-parametric Kruskal-Wallis test and the parametric Welch T-test were used to test the differences in defect density for technological and situational factors related to these projects. Results suggest technological and situational factors significantly impact the quality of software. Empirical findings revealed the following factors result in lower defect density: (1) project enhancements versus new project development; (2) smaller projects versus larger projects; (3) using a development methodology; (4) using later generation programming languages; (5) developing projects with in-house teams; (6) using an iterative development methodology versus a waterfall development methodology; (7) using larger development teams versus smaller teams; (8) using CASE tools; and (9) developing projects on a standalone platform versus developing on a client\server multiplatform.

An efficient heuristic algorithm for software module clustering optimization

In the lifecycle of software projects, maintenance tasks usually entail 75% of the total costs, where most efforts are spent in understanding the program. To improve the maintainability of software projects, the code is often divided into components, which are then grouped in different modules following good design principles, lowering coupling and increasing cohesion. The Software Module Clustering Problem (SMCP) is an optimization problem that looks for maximizing the modularity of software projects in the context of Search-Based Software Engineering. In the SMCP, projects are often modeled as graphs. Therefore, the SMCP can be interpreted as a graph partitioning problem, which is proved to be NP-hard. In this work, we propose a new heuristic algorithm for software modularization, based on a Greedy Randomized Adaptive Search Procedure with Variable Neighborhood Descent. We present a three-fold categorization of neighborhoods for the SMCP and leverage domain-specific information to filter unpromising solutions. Our proposal has been successfully tested over a dataset of real software projects, outperforming the previous state-of-the-art approach in terms of Modularization Quality in very short computing times. Therefore, it could be integrated in software development tools to improve the quality of software projects in real time.

Expert assessment of quality innovative software projects: choice of integrated index

The successful introduction of various information systems, technologies and innovative software projects into the management of the production activities of enterprises largely determined by the quality of their development. Expert systems used to assess the quality of information products. Many of them use for the resulting assessment the calculated values of the integral indicator of quality assessment. The authors substantiate a number of possible representations of the integral indicator for assessing the quality of innovative software projects and formulas for its calculation. The choice of formulas carried out depending on the presence of restrictions imposed on aggregated quality indicators by users or customers of information products and innovative software projects.

Novel similarity measures, entropy of intuitionistic fuzzy sets and their application in software quality evaluation

Intuitionistic fuzzy sets (IFSs), including member and nonmember functions, have many applications in managing uncertain information. The similarity measures of IFSs proposed to represent the similarity between different types of sensitive fuzzy information. However, some existing similarity measures do not meet the axioms of similarity. Moreover, in some cases, they could not be applied appropriately. In this study, we proposed some novel similarity measures of IFSs constructed by combining the exponential function of membership functions and the negative function of non-membership functions. In this paper, we also proposed a new entropy measure as a stepping-stone to calculate the weights of the criteria in the proposed multi-criteria decision-making (MCDM) model. The similarity measures used to rank alternatives in the model. Finally, we used this MCDM model to evaluate the quality of software projects.

Reliable Requirements Engineering Practices for COVID-19 Using Blockchain

Improvement in the requirements for engineering practices is needed in areas such as requirement elicitation, validation, prioritization, and negotiations between stakeholders to create successful projects for COVID-19 (coronavirus disease 2019) software. Many algorithms and techniques are used to create quality software projects, but they still need more improvement to work effectively for global pandemic COVID-19 software. By improving the reliability of requirement engineering practices using blockchain-based technology, the software will be reliable and will make it easier for the users working in a lockdown situation because of COVID-19. Therefore, our purpose is to identify the factors for reliable software engineering practices using blockchain-oriented technology for COVID-19 software. A systematic literature review is conducted to identify challenges and offer solutions. Through using blockchain-based technology for requirement engineering practices, the requirements will be gathered accurately and validated, and the conflicts between stakeholders will also be solved. It will improve the quality and reliability of COVID-19 software projects, which will help society work effectively from home. Improvement in the quality and reliability of COVID-19 software will improve users’ interest, and their working capacity will be increased.

Enhancement of the Capability Maturity Model for Improving the Quality of Software Projects in Developing Countries

Business process modeling was suggested by academia and implemented by the industry to trigger device requirements. Maturity models of software processes are also similar in the software industry. Therefore, business software requirement engineering approaches-based process should aim to fulfil with the maturity models. Although researchers in the area of Software Engineering, Project Management, and Business Intelligent have addressed planning and execution and a lot of research still needs to be done, in this study, we focused on doing a questionnaire based on different prominent universities, major companies, and software houses. The purpose of the questionnaire was to dig out the main factors that negatively affect software houses and their products and services, especially in the medical field. A questionnaire was generated to accomplish these reasons, and interviews were conducted to extract the key explanation why software developers and software houses in developing countries are not familiar with the CMMI (Capability Maturity Model Integration) model for medical software projects. This analysis attempted to figure out why the tech enterprises of developing countries are behind foreign competitors. We aimed to determine why software developers do not meet the international requirements for software. The novelty of the research is that attempts were made to strengthen the new approach by adding agile at level 3 to keep the local software industry appropriate and reveal the requirements for medical software life cycle study, because accurate decision support is really necessary for healthcare systems.

Impact of time pressure on software quality: A laboratory experiment on a game-theoretical model.

Research suggests the relationship between time pressure and software quality to be more complex than presumed. While software developers can adjust their output to improve observed performance at the expense of software quality, the latter has been found to increase with time pressure in case of work-pace dependent incentives. An untested, but widely disseminated game-theoretical model seeks to resolve this contradiction and hypothesizes that high rates of time pressure avoid so-called ‘shortcuts’, which occur in the form of imperfections induced by developers to meet unrealistically tight deadlines. We conduct two laboratory experiments to empirically test this model for the first time. Our results corroborate the model with regard to its suggestion that shortcuts can be reduced if developers perceive unrealistic deadlines as ever-present. However, we also show that the actual critical probability of unrealistic deadlines–the point at which shortcut taking is drastically reduced–is above the theoretical one. Although final conclusions on the impact of time pressure on software quality remain to be drawn, our results suggest that–considering the contingencies of our study–time pressure helps in striving for quality in software projects.

Quality Assessment of Standard and Customized COTS Products

Software quality is a very important aspect in evolving strategy for IT vendors involved in commercial off-the-shelf (COTS) (also referred as packaged software) product development. Software metrics are widely accepted measures for monitoring and managing the quality in software projects. Enterprise resource planning (ERP) systems are COTS products and attempt to integrate data and processes in organizations and often require extensive customization. Using software quality metrics already established in literature, software quality attributes defined by the quality model ISO/IEC 9126 were evaluated for a standard and a customized ERP product. This will help the ERP team to identify the specific quality attributes that were affected owing to customization. This research study infers that there exists a considerable impact of ERP system customization over the quality of ERP product. The implications of the findings for both practice and research are discussed, and possible areas of future research are identified.

Cross-projects software defect prediction using spotted hyena optimizer algorithm

Cross-projects software defect prediction improves the quality of new software projects or projects with a shortage of historical data. Therefore, various data mining techniques are recommended in this field. The classification accuracy issue is considered one of the most significant problems due to the shortage and heterogeneous in historical data. To address this challenge, this research utilizes a spotted hyena optimizer algorithm as a classifier to predict defects through cross-projects. Confidence and Support are utilized as a multi-objective fitness function to look for the best classification rules. These classification rules are used to predict defects for new projects or other projects with insufficient data. The datasets of NASA such as JM1, KC1, and KC2 are used. By applying spotted hyena optimizer algorithm as a classifier on one dataset and predicting defects in the other two datasets, accuracy is reported 84.6, 92.0, 82.4, 90.7, 86.6 and 81.8 for JM1, KC1, and KC2 respectively. These accuracy values are better than the most significant data mining techniques in the field such as Support Vector Machine, Naive Bayes, Boosting, C4.5, and Bagging. Also, the proposed research discusses other performance measures such as precision, recall, and f-measure. The conclusion proves that there are many features of McCabe and Halstead that have a strong impact to generate highly accurate predictors for defects such as McCabe’s line count of code, McCabe’s cyclomatic complexity, McCabe’s essential complexity, McCabe’s design complexity iv, Halstead’s effort, Halstead’s time estimator, Halstead’s line count, Halstead’s count of line of comments and total operators.

QUALITY OF SOFTWARE PROJECTS – A CASE STUDY

In software projects, many points that are overlooked such as time constraints and human factors are causing great problems in the future. By measuring the quality of software projects, problems that may arise in important parameters such as maintenance-repair, functionality and reliability can be eliminated. In this study, metrics that can be used for measuring maintainability quality attribute within the scope of ISO 9126 Quality Standard are examined. In order to perform the study, 40 open source object-oriented software was selected and code complexity analysis was performed. Values of metric sets such as Chidamber and Kemerer (CK), Lorenz and Kidd (LK) and McCabe's complex Suite were determined by the Understand Code Analysis tool. It was determined whether the obtained values exceeded the threshold values indicated in the literature. Frequencies of metrics passing threshold values were determined for 40 open source object-oriented software projects, and the consistency among the metrics was evaluated using WEKA Machine Learning Software and EXCEL Data Analysis Tool. When the results were evaluated, it was observed that in addition to CK metrics such as WMC, CBO, and RFC, which measure the maintainability quality attribute, NOC (CK), NIM (LK), and the ratio of comment/code metrics have been observed to yield significant measurement results.

A Change Recommendation Approach Using Change Patterns of a Corresponding Test File

Change recommendation improves the development speed and quality of software projects. Through change recommendation, software project developers can find the relevant source files that they must change for their modification tasks. In an existing change-recommendation approach based on the change history of source files, the reliability of the recommended change patterns for a source file is determined according to the change history of the source file. If a source file has insufficient change history to identify its change patterns or has frequently been changed with unrelated source files, the existing change-recommendation approach cannot identify meaningful change patterns for the source file. In this paper, we propose a novel change-recommendation approach to resolve the limitation of the existing change-recommendation method. The basic idea of the proposed approach is to consider the change history of a test file corresponding to a given source file. First, the proposed approach identifies the test file corresponding to a given source file by using a source–test traceability linking method based on the popular naming convention rule. Then, the change patterns of the source and test files are identified according to their change histories. Finally, a set of change recommendations is constructed using the identified change patterns. In an experiment involving six open-source projects, the accuracy of the proposed approach is evaluated. The results show that the accuracy of the proposed approach can be significantly improved from 21% to 62% compared with the existing approach.

A complexity metric for object-oriented software

ABSTRACT With the continuous expansion of software scale, the complexity of software is getting increasing. The relationship between the models of large software projects is very complicated. In the development of software, how to make a reasonable evaluation of the quality of software projects has become a very important and urgent problem. Class diagram as a very important diagram in the process of software design, its reasonable design has a remarkable effect on the software complexity. To study the complexity of the class diagrams, this paper proposes maximum deviation-based multi-attribute decision method to measure class diagrams. By using the method of maximizing deviations, we can measure the complexity of the object-oriented software scientifically and reasonably. And then through case analysis, Pearson simple correlation coefficient was used to analyze the correlation between the existing three measurement methods and the metric of practical experience, whose results show that while measuring the object-oriented software, maximizing deviation method is more in accordance with practical experience.

Soft skills requirements in mobile applications development employment market

The soft skills of developers have a major influence on the quality of software product and project. However, which soft skills are important for mobile applications development remains unknown. Additionally, it is necessary to examine the differences of soft skills requirements between traditional software and mobile applications development. In this article, based on text mining including word segmentation, similarity calculation and clustering analysis, we analyse lots of advertisements, and extract 13 categories of soft skills requirements for mobile applications development. We also compare the categories with those for traditional software development. We find that communication and teamwork are still the most important two soft skills. However, fast learning is more important for mobile developers, and we identified four soft skills that are not proposed before. Additionally, season has a minor impact on soft skills requirements of mobile applications development.

Grouping environmental factors influencing individual decision‐making behavior in software projects: A cluster analysis

AbstractAn individual's decision‐making behavior is heavily influenced by and adapted to external environmental factors. Given that software development is a human‐centered activity, individual decision‐making behavior may affect the software project quality. Although environmental factors affecting decision‐making behavior in software projects have been identified in prior literature, there is not yet an objective and a full taxonomy of these factors. Thus, it is not trivial to manage these complex and diverse factors. To address this deficiency, we first design a semantic similarity algorithm between words by utilizing the synonymy and hypernymy relationships in WordNet. Further, we propose a method to measure semantic similarity between phrases and apply it into k‐means clustering algorithm to group these factors. Subsequently, we obtain a taxonomy of the environmental factors affecting individual decision‐making behavior in software projects, which includes 11 broad categories, each containing 2 to 5 sub‐categories. The taxonomy presented herein is obtained by an objective method, and quite comprehensive, with appropriate references provided. The taxonomy holds significant value for researchers and practitioners; it can help them to better understand the major aspects of environmental factors, also to predict and guide the behavior of individuals during decision making towards a successful completion of software projects.

A Hybrid eBusiness Software Metrics Framework for Decision Making in Cloud Computing Environment

Developing high-quality software is essential for eBusiness organizations to cope with drastic market competition. With the development of cloud computing technologies, eBusiness systems and applications pay more attention to open endedness. In a cloud computing environment, eBusiness systems have the ability to provide information technology resources on demand. Traditional software metric methods in distributed systems and applications are technical and project driven, making the market demand and internal practical operation not perfectly balanced within a cloud-computing-based eBusiness corporation. To address this issue, this paper presents a hybrid framework based on the goal/question/metric paradigm to evaluate the quality and efficiency of previous software products, projects, and development organizations in a cloud computing environment. In our approach, to support decision making at the project and organization levels, three angular metrics are used, i.e., project metrics, product metrics, and organization metrics. Furthermore, an improved radial-basis-function -based model is also provided to manage existing projects and design new projects. Experimental results on a well-known eBusiness organization show that the proposed framework is effective, efficient, and operational. Moreover, using the described decision-making algorithm, the predicted data are very close to actual results on the software cost, the fault rate, the development workload, etc., which are greatly helpful in achieving high-quality software.