Search

Project risk management which has been rarely considered, especially in software projects, is a crucial process to complete projects successfully. This paper aims to propose a novel project risk management framework both to evaluate the project risks and to predict the success or failure of software projects based on their risk. This new framework uses Machine Learning (ML) and Multi-Attribute Decision-Making under a cloud model to effectively manage uncertainty. Based on the proposed framework, in the first stage, the important risks of the software projects are identified by an organized approach. Then, the risks are evaluated based on their probability and impacts on time, cost, and quality. In the second stage, the obtained results of the previous stage are entered into a new COPRAS method under the cloud model to rank the risks. Then, the risks are classified into various groups according to their rank. It helps project managers to gain a profound awareness of their high-priority project risks. In this paper, data on risks for fifty software projects has been collected. All the steps of the second stage are implemented on these projects in order to assess their risks. As a result, a data set whose features are nine types of software project risks and the label of success or failure of the projects is created. To recognize the pattern between risks' values and the success or failure of the projects, various efficient ML algorithms such as Naive Bayes, Logistic regression, Decision Tree, Bagging, Random Forest, and AdaBoost are applied. This framework can predict the success or failure of software projects based on their risks with good accuracy. The results depict that the Naive Bayes algorithm has the best results compared to others. [ABSTRACT FROM AUTHOR]

The process of software development is complex, and one of its most critical aspects is estimating the amount of work required for various projects. However, accurately defining the exact amount of work needed in the early stages of production can be challenging. Researchers have been working on creating different machine and deep learning models to address this issue. These models, including single-approach models and multi-model ensembles, utilize optimization strategies to provide precise predictions. We propose a hybrid particle swarm optimization (PSO) based artificial neural networks (ANNs) model for software effort estimation (SEE), which has shown to outperform existing models. This model was tested on various datasets such as Albrecht, China Desharnais, Kemerer Kitchenham Maxwell, and Cocomo81. The hybrid PSO-optimized ANNs model has exhibited exceptional accuracy, as evidenced by consistently high R-squared (R2) values across multiple datasets. Additionally, the model has displayed low root mean square error (RMSE) and mean absolute error (MAE) values, indicating precise predictions. These outcomes affirm the model's precision and effectiveness. The model's small MAE further confirms its accuracy in predicting the required work during software development. With these remarkable results, the hybrid PSOoptimized ANNs model will undoubtedly play a crucial role in software development processes, providing accurate and precise predictions of the required work. [ABSTRACT FROM AUTHOR]

The globalization of software development processes and outsourcing of software development projects worldwide has led to the creation of virtual teams. These teams are fraught with challenges due to the geographical dispersion of members, differences in time zones, and different cultures. Virtual teams have become an essential aspect of software development projects. Even after many advancements in this area and the challenges posed by virtual teams, the current rate of software project success generates the need to study this area. A quantitative approach is used to study the responses provided by team members working as a part of a virtual team and involved in software projects in the Indian IT industry. The responses received were analyzed using regression analysis to propose and evaluate the proposed research model. The finding of the research indicates that virtual team attributes, such as technological augmentation, team dynamics, diversity, and strategic decision-making, have a significant impact on software project outcomes. In a Virtual team setting, the model developed during this research will serve as a valuable resource for the project leaders to comprehend the factors that influence project success and assist in implementing policies towards successful software projects. The research findings substantiate the previous research done for Virtual teams in developed countries, and the dimensions are evaluated in a completely new environment for developing countries like India. [ABSTRACT FROM AUTHOR]

Looking at the history of civilization from past to present, the concepts of project and project management are frequently encountered. Thanks to project management, businesses can work target-oriented, provide high motivation, facilitate internal control and provide a significant increase in quality. The concept of software, born with the development of technology, penetrates our lives more and more day by day. The globalizing world has brought together the concepts of software and project management and integrated them. Software projects require special management techniques because of their content. For this reason, new methods have emerged over time in the management of software projects. In this study, the concept of the project, the development of project management from its birth over the years, project management in software projects, software development life cycles, and the Waterfall Model and Agile Approach, which are two methods used in software projects, are discussed. The perception of the Waterfall Model and Agile Approach was evaluated with the survey study created with the participation of 145 employees from the software industry. Thus, it is aimed to support managers to choose an effective and efficient method at the point of deciding which method to proceed in software project management.

Predicting software faults at the primary stage is a challenging role for software engineers and tech industries. During the development of software projects, it is necessary to predict the number of probable faults to have occurred on software rather than detecting whether the software modules are faulty or not. Discovering the number of expected faults helps software professionals to develop more reliable and high-quality software systems. However, the prediction model's performance gets affected while dealing with complicated software projects with increased cost factors such as time, effort, and resources. Therefore, to address the issue associated with handling complex software projects, a novel weighted dual cross-recurrent network-based levy sparrow search (WDCRN-LSS) model is proposed in this paper. The WDCRN-LSS approach by learning the data features with optimal hyperparameters accurately predicts the expected software faults in an earlier phase. Here, 17 PROMISE datasets containing 20 features each are utilized as input data for the proposed WDCRN-LSS model. The data inconsistencies are eliminated and then transformed to a suitable format for training through normalization, data transformation, and label encoding procedures. The preprocessed data are then trained using the proposed WDCRN-LSS model for the prediction of the expected number of software faults in the projects. With the excellent learning capability of feature representations, the proposed WDCRN-LSS model predicts software faults on upcoming/under-development software projects precisely. Thus, the proposed WDCRN-LSS model enhances software quality and minimizes cost factors such as time, resources, and effort that are depleted in developing software. The proposed WDCRN-LSS model's efficiency is investigated by utilizing evaluation measures namely error rate, precision, recall, F1-measure, the area under the curve, accuracy, and specificity. The experimental result manifests the efficiency of the proposed WDCRN-LSS model with a software fault detection accuracy of about 98.1%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Turkish Operations Management (JTOM) is the property of Ankara Yildirim Beyazit University Journal of Turkish Operations Management (JTOM) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Defective modules that cause software execution failures are common in large software projects. Source code for a significant number of modules may be found in several software repositories. This software repository includes each module's software metrics and the module's faulty status. Software companies face a considerable problem detecting defects in sizeable and complex programming code. In addition, many international reports, such as the comprehensive human appraisal for originating (CHAOS) report, have mentioned that there are countless reasons for the failure of software projects, including the inability to detect errors and defects in the programming code of those projects at an early stage. This research employs a statistical analysis technique to reveal the characteristics that indicate the faulty status of software modules. It is recommended that statistical analysis models derived from the retrieved information be merged with existing project metrics and bug data to improve prediction. When all algorithms are merged with weighted votes, the results indicate enhanced prediction abilities. The proposed statistical analysis outperforms the state-of-the-art method (association rule, decision tree, Naive Bayes, and neural network) in terms of accuracy by 9.1%, 10.3%, 13.1%, and 13.1%, respectively. [ABSTRACT FROM AUTHOR]

The objective of this study is to provide a comprehensive comparison between open-source projects and closed-source projects by examining several key aspects, including the number of contributors or employees, the number of features introduced per project, the number of vulnerabilities present in the software, revenue or profit, and project management techniques. By comparing these aspects across a range of open-source and closed-source projects, we aim to evaluate the potential for these distribution models to complement each other and identify the contexts in which one model may be more effective than the other. This analysis seeks to provide valuable insights for stakeholders involved in software development, including developers, project managers, and decision-makers. In conclusion, this study provides a nuanced comparison of open-source and closed-source software projects, addressing key areas like contributor numbers, feature development, vulnerabilities, revenue, and project management. The insights gained are intended to guide stakeholders in software development, helping to discern which model is more effective in various contexts and how they might complement each other. [ABSTRACT FROM AUTHOR]

Software estimation is a vital yet challenging project management activity. Various methods, from empirical to algorithmic, have been developed to fit different development contexts, from plan-driven to agile. Recently, machine learning techniques have shown potential in this realm but are still underexplored, especially for individual task estimation. We investigate the use of machine learning techniques in predicting task effort and duration in software projects to assess their applicability and effectiveness in production environments, identify the best-performing algorithms, and pinpoint key input variables (features) for predictions. We conducted experiments with datasets of various sizes and structures exported from three project management tools used by partner companies. For each dataset, we trained regression models for predicting the effort and duration of individual tasks using eight machine learning algorithms. The models were validated using k-fold cross-validation and evaluated with several metrics. Ensemble algorithms like Random Forest, Extra Trees Regressor, and XGBoost consistently outperformed non-ensemble ones across the three datasets. However, the estimation accuracy and feature importance varied significantly across datasets, with a Mean Magnitude of Relative Error (MMRE) ranging from 0.11 to 9.45 across the datasets and target variables. Nevertheless, even in the worst-performing dataset, effort estimates aggregated to the project level showed good accuracy, with MMRE = 0.23. Machine learning algorithms, especially ensemble ones, seem to be a viable option for estimating the effort and duration of individual tasks in software projects. However, the quality of the estimates and the relevant features may depend largely on the characteristics of the available datasets and underlying projects. Nevertheless, even when the accuracy of individual estimates is poor, the aggregated estimates at the project level may present a good accuracy due to error compensation.

In recent years, there has been a significant surge in interest in the incorporation of artificial intelligence (AI) within the field of software engineering. This phenomenon can be attributed to the fact that AI has become ubiquitous and increasingly accessible, thereby finding effective application across various pivotal facets of software systems. Its contribution extends not only to the creation of novel functionalities but also to the enhancement of existing processes within software projects, often resulting in substantially improved adaptability to specific user requirements. Within this paper, we provide an overview of the application of AI tools within one of the critical domains of software project management - risk management. To achieve this objective, a bibliometric analysis of literature pertaining to risk management in software projects employing AI tools has been conducted. The primary aim of this study is to identify and analyze key trends, authors, journals, and keywords within this multidisciplinary domain, in order to gain a better understanding of the progress and relevance of research concerning risk management in software projects utilizing AI tools. The methodology encompasses a review of pertinent databases and the identification of relevant publications using keywords associated with software projects, risk management, and artificial intelligence. Quantitative parameters such as the number of published works, author collaborations, citation frequency, and the distribution of articles over time and across journals are analyzed.

Copyright of Revista Educación en Ingeniería is the property of Asociacion Colombiana de Facultades de Ingenieria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to examine the impact of employee resilience and job engagement on project success, and the mediating role of innovative work behavior. The study draws on a sample of 170 from the population of 304 information technology (IT) companies in Lahore Pakistan to test the proposed hypotheses. Smart PLS statistical software has been used to calculate the structural modelling equation results for the subject study. The results indicate that both employee resilience and job engagement have a positive impact on project success. Furthermore, innovative work behavior partially mediates the relationship between employee resilience and project success, as well as between job engagement and project success. These findings highlight the importance of fostering employee resilience and job engagement to promote project success and the role of innovative work behavior in facilitating this relationship. Organizations can benefit from implementing strategies that enhance employee resilience and job engagement while promoting a culture of innovation to achieve project success. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Business & Projects / Revista de Gestão e Projetos is the property of Revista de Gestao e Projetos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: This study aims to investigate the effects of job stress and job satisfaction on employee performance considering emotional intelligence as a mediating variable. The study focuses on the factors that affect job stress and job satisfaction such as interpersonal relationships, management support, the relations between management and employees, the function of the group, and work related to employees. Method: The data were collected using online questionnaires from participants working on software projects in Palestine. Based on a sample of 250 responses with a 62.5% effective response rate, and using partial least squares structural equation modeling. Result: The results confirm that the job satisfaction, job stress, and emotional intelligence is a key enabler of employee performance. Moreover, emotional intelligence mediates the relationship between job stress and employee performance. The results also confirm that job stress and job satisfaction are important factors affecting employee performance and productivity. Conclusion: This study contributes to the literature on performance management by addressing the role of emotional intelligence in improving employee performance and productivity. [ABSTRACT FROM AUTHOR]

Software Development Projects (SDPs) are being conducted at a rapid rate in response to the Fourth Industrial Revolution (4IR), even though SDPs are associated with very high failure rates. Their failure rate of SDPs has been declared as an International Crisis. SDPs are knowledge intensive in nature, whereby knowledge is essential when performing sociotechnical tasks within SDPs. One of the reasons for the failure of SDPs is limited sharing of tacit knowledge within SDP teams. Limited tacit knowledge sharing within SDP teams results in the teams not being in position to perform their socio-technical tasks to the required standards which leads to success of SDPs. The main purpose of this research study was to identify the team-oriented barriers that limit the sharing of tacit knowledge within SDP teams. The researchers conducted a qualitative study and relied on qualitative data to achieve the objectives of the study. The qualitative data were collected through interviews and thematic data analysis was conducted to generate the results. A total of seven team-oriented barriers were identified includes team-culture, team-orientation, team-dispersion, team-cohesion, team-characteristics, trust, and communication. These issues are related to each other and influence one another in a positive way. These barriers stem from the entire SDP team, which is responsible for ensuring the success of SDPs. [ABSTRACT FROM AUTHOR]

Software development requires the assimilation of team members' diverse knowledge, ideas, and skills for innovative solutions. A software team's knowledge integration is affected by project characteristics, such as scale and interdependence, and the team's use of information technology (IT). We examine how contingencies embedded in these dimensions influence team's knowledge integration. We argue and show that IT-use plays either complementary or substitutive role in moderating the impacts of project scale and project interdependence on knowledge integration. [ABSTRACT FROM AUTHOR]

The topic of critical success factors draws high attention of both academia and practitioners working in the software industry. Literature review shows that existing models of critical success factors for software projects are more concentrated on specific categories of success factors, neglecting the interaction that exists within these models between different groups of factors. The purpose of this paper is to present a new concept of interaction-based model for identification and evaluation of critical success factors for software projects. The approach is based on conjoint use of graph theory and Design Structure Matrices to highlight important interactions of a project system. The key result of the paper is the tool for evaluation of the interaction between critical success factors through quantification and the analysis of the possible propagation of specific factor within project success factor network, which will make possible the identification of the critical factors with the highest influence on the entire project system.

Since the appearance of the Agile Manifesto for more successful software development, the agile concept of thinking in management has evolved and today we have far more developed forms of agile management, agile tools and methods that have helped many teams and companies to adopt and apply agile practices. The aim of this paper is to review the different levels of implementation of agile management and show the main advantages that the implementation of agile management has. Through the literature review, the concept of agile project management, organizational agility and business agility as the highest stage of implementation of the agile concept in companies are presented. The paper analyzes the classic and agile concepts of project management, shows the process of agile project management as well as the main roles and benefits. Organizational agility is shown through roles and organizational structure, as well as through the most common ways of agile work management. Finally, business agility is explained and the main areas where changes need to be made to establish it at the company level are discussed.

Software project scope definition is complicated due to the diversity and magnitude of the information needed. An inadequate scope definition often results in project failure as it continues to emerge as the major cause of delays, changes/rework, and cost and schedule overruns. Literature mentions different tools and methods to verify, quantify, and control software scope definition. However, none of these methods and tools help in defining a complete scope. Since a well-defined scope in the early stages is a core ingredient for project success, therefore, previously we developed a method that includes 45 elements of the software project scope definition. Although these elements are noteworthy for project scope, some may influence others; and should be concentrated on more when defining the scope of the software projects. The objective of this present study is that it builds on our previous research and uses Interpretive Structural Modeling (ISM) to extract the interrelationships among the elements and Decision Making Trial and Evaluation Laboratory (DEMATEL) to determine the intensity of these relationships. Experts from academia and the software development industry were consulted to identify the relationships among the elements. ISM-DEMATEL approach indicates that the project manager's competence is the main driver. Further, requirements, stakeholders' expectations, cost estimates, project schedule, resource estimation, project summary, communication, consultation, top management support are among the most influential elements that are critical to focus on. Moreover, the findings of the study provide project managers with a better understanding of the elements and their interconnections thus, helping in achieving a better scope definition.

Context: The definition and prioritization of indicators is now a common reality and an integral part of the evolution of the strategic, technical and business processes of any organization, whether public or private. Purpose: This paper proposes a new model regarding the definition and prioritization of indicators. Herein, we also investigate the definition and prioritization models currently adopted by academia and industry, and analyze the context of the proposed strategies against the traditional view of indicator definition currently adopted. In addition, we conducted a survey with organizations that had well-defined indicator management processes, seeking to identify customer expectations with a new indicator management model proposed by this work. Method: To gather evidence, we defined a methodology that relates the literature review and an exploratory case study with the application of an experiment. Driven by a set of research questions, this methodology comprised four main phases: planning, literature review, experiment execution and documentation of results. The method used is supported by some techniques, such as design thinking, design sprint and the Cynefin framework. Results: The analysis of the results was carried out in two different ways: Through the verification of the achievement of specific objectives and through a questionnaire applied to assess the degrees of perception of all employees who participated in the work. Regarding the specific objectives, it is clear that most of the objectives were achieved. Regarding the applied questionnaire, it is clear that, although the collaborators did not have adequate knowledge regarding the conceptual and practical aspects of some approaches used in the proposed model, there was a general perception that the model, in fact, supported top management for decision making. For professionals, the proposed model has a restricted scope; that is, it does not serve all types of organizations. Conclusion: The model proposed in this work proved to be effective, considering that the indicators were defined, prioritized and optimized, with a focus on the user experience. As future work, we intend to expand the scope of the model's performance, evaluating business indicators alongside IT indicators. [ABSTRACT FROM AUTHOR]

We examine the role of sentry and guard activities in outsourced software development. Sentry activities are designed to regulate the inflow of external information to the project teams and guard activities are designed to manage the outflow of teams’ information and resources to external sources. The use of sentry and guard activities has been examined in teams in other contexts such as new product development, but their role and relationship to performance in software development teams is not well understood. We hypothesize and test curvilinear relationships between these activities and knowledge integration in vendor development teams. We also examine how these effects vary under conditions of greater project uncertainty. We tested the hypotheses using data from 139 vendor development teams drawn from sixteen Indian software companies. Results highlight complex curvilinear associations among sentry and guard activities, and knowledge integration, which are further impacted by the level of uncertainty that the project team faces. We recommend that carefully calibrating sentry and guard processes will help vendor development teams enhance project outcomes. [ABSTRACT FROM PUBLISHER]

In recent years, one of the most important topics in the field of software development projects is rethinking success in context of increasing complexity and failure of software projects. Literature review shows that there is no clear line between success and failure, especially considering that a project can be both successful in one area and unsuccessful in another. In addition, the increasing complexity, context and broader conceptualization of projects, required both change in project and success definition. The aim of this paper is to perform literature review in field of software project success definition, as well as to propose a multidimensional construct that includes project management success, project success and strategic project success as three dimensions for measuring software's project success.

The goal of this paper was to identify factors of development effort and productivity and investigate the nature of these relationships using the current release of the ISBSG dataset. In particular, statistical measures of correlations and associations, single-predictor linear regression models, and, most importantly, a moderation analysis were used. Performed analysis demonstrated which attributes are in strong relationships with effort and productivity, investigated the explainability of single-predictor models, and discussed if and how particular attributes moderate the strongest relationships reflected in these single-predictor models. [ABSTRACT FROM AUTHOR]

Agile methods and Agile scaling frameworks have become a solution for software-developing organizations striving to improve the success of software projects. Agile methods were developed for small projects, but due to their benefits, even large software-developing organizations have adopted them to scale their software projects. This quantitative study was undertaken to deepen the researchers' understanding of the critical success factors and challenges of Scaled Agile from the South African perspective. A simple random sampling method was used. Data was collected with the use of an online structured questionnaire and the response rate was 70%. The results reveal that customer satisfaction remains at the epicenter of adopting Scaled Agile methods. Lack of top management support remains the major challenge in adopting Scaled Agile. The results reveal some notable changes when it comes to the most adopted Agile scaling framework. [ABSTRACT FROM AUTHOR]

Software projects are still failing at an alarming rate and do not provide value to the organisation at large. This has been the case for the last decade. Software projects use predominantly Waterfall as a methodology. This raises the question whether new ways of working can be introduced to improve the success rate. One such new way is Agile as an approach to developing software. A survey was done to determine whether Agile projects are more successful than Waterfall projects, thus contrasting the old and the new ways of working. Some 617 software projects were evaluated to determine the success rate based on the methodology used. Success was measured on a continuum of five levels and not just the triple constraint. The results imply that Agile projects are more successful than Waterfall projects to some extent, but that there are still concerns that need to be addressed.

User satisfaction is an important feature of software quality. However, it was rarely studied in software engineering literature. By enhancing earlier research this paper focuses on predicting user satisfaction with machine learning techniques using software development data from an extended ISBSG dataset. This study involved building, evaluating and comparing a total of 15,600 prediction schemes. Each scheme consists of a different combination of its components: manual feature preselection, handling missing values, outlier elimination, value normalization, automated feature selection, and a classifier. The research procedure involved a 10-fold cross-validation and separate testing, both repeated 10 times, to train and to evaluate each prediction scheme. Achieved level of accuracy for best performing schemes expressed by Matthews correlation coefficient was about 0.5 in the cross-validation and about 0.5–0.6 in the testing stage. The study identified the most accurate settings for components of prediction schemes.

The study presented in this paper was developed in an academic environment. All subjects involved in this research are enrolled in the Information Systems Master's Degree of the University of Minho. The main objective of this study is to demonstrate the need for the continuity of studies about the risks presented in software development projects. For this purpose, we analyzed more than four hundred risks and about one hundred problems faced and documented by the working groups. With the collected data we defined a list of twenty risks and conducted a comparative study of these risks with others already formalized in previous industrial studies. The comparison data was analyzed and interpreted, and important conclusions were made. [ABSTRACT FROM AUTHOR]

In the teaching of subjects related to software projects, the incorporation of active learning methodologies has brought good results in computer science and computing engineering careers. During the last 20 years, the Learning plus Service (A + S) methodology has contributed significantly to these results. This article describes the results of an active teaching strategy in the Programming Workshop subject of the Technical Engineering in Computing and Informatics career. In the strategy, the A + S methodology has been used for a solidary software development, in order to giving support to the community. Using this strategy, skills such as software design and implementation with object-oriented programming and software quality have been developed. The work has also been oriented to develop other skills such as group work, oral and written communication and self-management, all these skills necessary for the correct professional development of the 21st century. [ABSTRACT FROM AUTHOR]

Copyright of Future Studies Research Journal: Trends & Strategies is the property of Future Studies Research Journal: Trends & Strategies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is well‐documented that many software projects deliver fewer benefits than planned. However prior research has had a stronger focus on the ability to deliver within budget, on time and with the specified functionality, than on what to do to successfully deliver client benefits. The authors have conducted a survey collecting information about benefits management practices, agile practices, use of contracts, and the perceived success in delivery of client benefits. The authors received responses from 83 software professionals with information about 73 recent and 74 older software projects. There was no statistically significant improvement of the delivered client benefits from the older to the recent projects. Statistically significant findings, applying a general linear model‐based analysis, include that the degree of success in delivering client benefits is connected to a project having: (i) a plan for how to realise the benefits, (ii) implemented practices for benefits management during project execution, (iii) frequent deliveries to production during the project execution, and (iv) a process for the evaluation of realised benefits after project completion. The authors argue that greater use of these practices represents a potential for organisations to increase their success in delivering benefits from software projects. [ABSTRACT FROM AUTHOR]