Software Project Risk Research Articles

A Systematic Review of Risk Management Tools and Techniques in Software Projects

The crafting of software is a continual procedure, and the success of each step of that process is contingent on effective management. Despite this, numerous organizations need help developing e-service systems, frequently dealing with budget constraints and tight deadlines. The lack of focus on risk management in software projects is likely to blame for these failures. Management of risks is crucial to ensuring the success and efficacy of software development projects, as it assists in identifying areas of vulnerability and provides valuable insights into the project's most important aspects. This study identifies and analyzes tools and techniques to support software development projects’ risk management activity. A systematic literature review (SLR) methodology was employed to collect and evaluate relevant research articles. The findings highlight various risk management tools and techniques, including brainstorming, root cause analysis, risk probability assessment, artificial intelligence, and risk response planning. These tools and techniques contribute to identifying, analyzing, planning, and controlling risks in software projects. The research provides insights into the state of the art in risk management. It complements previous studies by offering practical guidance on software development project risk management tools and techniques.

A Data-Driven Artificial Neural Network Approach to Software Project Risk Assessment

In the realm of software project management, predicting and mitigating risks are pivotal for successful project execution. Traditional risk assessment methods have limitations in handling complex and dynamic software projects. This study presents a novel approach that leverages artificial neural networks (ANNs) to enhance risk prediction accuracy. We utilize historical project data, encompassing project complexity, financial factors, performance metrics, schedule adherence, and user-related variables, to train the ANN model. Our approach involves optimizing the ANN architecture, with various configurations tested to identify the most effective setup. We compare the performance of mean squared error (MSE) and mean absolute error (MAE) as error functions and find that MAE yields superior results. Furthermore, we demonstrate the effectiveness of our model through comprehensive risk assessment. We predict both the overall project risk and individual risk factors, providing project managers with a valuable tool for risk mitigation. Validation results confirm the robustness of our approach when applied to previously unseen data. The achieved accuracy of 97.12% (or 99.12% with uncertainty consideration) underscores the potential of ANNs in risk management. This research contributes to the software project management field by offering an innovative and highly accurate risk assessment model. It empowers project managers to make informed decisions and proactively address potential risks, ultimately enhancing project success.

Application of Kano Model in Risk Identification of Software Development Projects

Risk management and identification of software projects is an important factor in software project management. Software project risks will affect the implementation of the project plans, and if they do, they will affect the project progress, increase the project costs, and even make the software project unfeasible. Risk occurrence can be minimized if risk management and identification are implemented in a project. However, in China, many software development companies do not have the awareness of risk management and identification, or use inappropriate management technology, resulting in software development projects are often delayed, beyond the budget or even failed. Successful project management requires good risk management and identification of projects. This study adopts the Ko model to scientifically and reasonably determine the priority of risk management of software projects, and take corresponding countermeasures for different risk factors, to ensure the smooth implementation of software projects, and then ensure the completion of them and the realization of objectives. This ensures the successful completion of software projects and optimal management with limited resources.

Analysis of Risk Factors in Global Software Development: A Cross-Continental Study Using Modified Firefly Algorithm.

In today's competitive world, software organizations are moving towards global software development (GSD). This became even more significant in times such as COVID-19 pandemic, where team members residing in different geographical locations and from different cultures had to work from home to carry on their tasks and responsibilities as travelling was restricted. These teams are distributed in nature and work on the same set of goals and objectives. Some of the key challenges which software practitioners face in GSD environment are cultural differences, communication issues, use of different software models, temporal and spatial distance, and risk factors. Risks can be considered as a biggest challenge of other challenges, but not many researchers have addressed risks related to time, cost, and resources. In this research paper, a comprehensive analysis of software project risk factors in GSD environment has been performed. Based on the literature review, 54 risk factors were identified in the context of software development. These were further classified by practitioners into three dimensions, i.e., time, cost, and resource. A Pareto analysis has been performed to discover the most important risk factors, which could have bad impact on software projects. Furthermore, a modified firefly algorithm has been designed and implemented to evaluate and prioritize the pertinent risk factors obtained after the Pareto analysis. All important risks have been prioritized according to the fitness values of individual risks. The top three risks are “failure to provide resources,” “cultural differences of participants,” and “inadequately trained development team members.”

Framework of Goal-driven Risk Management in Software Development Projects Using the Socio-technical Systems Approach

Previous studies have identified the influence of goal agreement on requirement elicitation in software projects, but they have not addressed how it influences risks in projects. The purpose of this study is to examine the influence of goal identification and agreement on software project risks. With socio-technical systems theory as the backdrop, we used a goal-oriented perspective to understand how the social subsystem influences the technical subsystem of a software development project. In an empirical study with 411 responses from software project developers and managers in India and using structural equation modelling, the causal linkages between goal orientation and project risk variables have been examined. The results indicated that the early adoption of goal-driven risk management reduces the social context risks which, in turn, reduces the project management risk. The study also revealed that the product artefact is independent of whether the system was completed within budget and schedule. To design the risk management strategy, project managers should consider the relationship between social context risk and project management risk and the implications of these relationships on project performance.

Impact of Risk Determinants on the Perceived Performance of Software Projects in Emerging Economies

Effective control of risk factors ensures the performance of projects in any industry. Risk factors can influence software projects of any size and type. This research aims to identify and examine different risk factors associated with projects in the software industry. The relationship between Software Project Risks (SPRs) and Perceived Project Performance (PPP) measures is determined. This study is based on a survey approach, and a questionnaire is used to record opinions and responses from 199 software professionals working in the Pakistan software industry. The results showed that the association between SPRs and PPP measures is statistically significant, and there exist a positive relationship. It is concluded that an increase in understanding of SPRs can increase PPP measures used to evaluate the software project. The results will help researchers and professionals in understanding the impact of different risk factors on software projects' perceived performance.

FPA evaluation of digital projects using IFPUG Method

This article considers Function Point Analysis (FPA) as a tool for price substantiation and specification of liabilities for development of digital project, since each new project in the company is analyzed. Information acquired during the analysis forms basis for adoption of critical decisions regarding complexity, resources, frames, terms, expenses, etc. Even strictly defined projects may fall out at later stages without accurate analytical method. Reducing risks of software projects is the most important issue. In general, this starts from definition of exact measures regarding scale, productivity, duration, quality, and other key performances of project efficiency. Advanced methods of analysis, such as FPA, provide clear presentation of each of these metrics related mainly with the amount of project, personnel, expenses and time, which supports management, control, adjustment of software development at early stages of planning.

Do scaling agile frameworks address global software development risks? An empirical study

Driven by the need to coordinate activities of multiple agile development teams cooperating to produce a large software product, software-intensive organizations are turning to scaling agile software development frameworks. Despite the growing adoption of various scaling agile frameworks, there is little empirical evidence of how effective their practices are in mitigating risk, especially in global software development (GSD), where project failure is a known problem.In this study, we develop a GSD Risk Catalog of 63 risks to assess the degree to which two scaling agile frameworks–Disciplined Agile Delivery (DAD) and the Scaled Agile Framework (SAFe)–address software project risks in GSD. We examined data from two longitudinal case studies implementing each framework to identify the extent to which the framework practices address GSD risks.Scaling agile frameworks appear to help companies eliminate or mitigate many traditional risks in GSD, especially relating to users and customers. However, several important risks were not eliminated or mitigated. These persistent risks in the main belonged to the Environment quadrant highlighting the inherent risk in developing software across geographic boundaries. Perhaps these frameworks (and arguably any framework), would have difficulty alleviating, issues that appear to be outside the immediate control of the organization.

Managing Software Project Risks (Implementation Phase) with Proposed Stepwise Regression Analysis Techniques

Regardless how much effort we put for the success of software projects, many software projects have very high failure rate and risks are everywhere in life. Risk is not always avoidable, but it is controllable. The aim of this paper is to present new techniques by which we can study the impact of different risk management techniques and different risk factors on software development projects. The new technique uses the stepwise regression to managing the risks in a software project and reducing risk with software process improvement. Top ten software risk factors (Implementation phase) and thirty control factors were presented to respondents. The results show that all risks in software projects were important in software project manager perspective, whereas all controls are used most of time, and often. These tests were performed using regression analysis to compare the controls to each of the risk factors to determine if they are effective in mitigating the occurrence of each risk factor and selecting best model. We referred the risk management techniques were mitigated on ten top software risk factors in Table XLVI. The study has been conducted on a group of software project managers. Successful project risk management will greatly improve the probability of project success.

A data-driven risk measurement model of software developer turnover

During the software development life cycle, the turnover of software developers is one of the critical risks that may lead to severe problems (such as postponement and failure of projects), which is often ignored by many professionals. To address this problem, we focus on the uncertainty of turnover risk of software developer (TRSD) and its loss incurred to projects. To tackle this problem, we propose a method to quantify the uncertain risks related to developer turnover, including resignation and replacement. Additionally, to calculate the extent of loss caused by TRSD, we employed machine learning, natural language processing, and data mining techniques to identify software development activities and establish the importance of developers by mining and analyzing the commit event logs. Moreover, based on the information entropy theory, we established a risk measurement model of TRSD that can be used to measure the risk level of each developer and the holistic risk of ongoing software projects. Finally, we validated the feasibility and efficacy through a case study.

Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network

Software supports continuous economic growth but has risks of uncertainty. In order to improve the risk-assessing accuracy of software project development, this paper proposes an assessment model based on the combination of backpropagation neural network (BPNN) and rough set theory (RST). First, a risk list with 35 risk factors were grouped into six risk categories via the brainstorming method and the original sample data set was constructed according to the initial risk list. Subsequently, an attribute reduction algorithm of the rough set was used to eliminate the redundancy attributes from the original sample dataset. The input factors of the software project risk assessment model could be reduced from thirty-five to twelve by the attribute reduction. Finally, the refined sample data subset was used to train the BPNN and the test sample data subset was used to verify the trained BPNN. The test results showed that the proposed joint model could achieve a better assessment than the model based only on the BPNN.

A Strategy Using Continuous Simulation to Mitigate Effort Estimation Risks in Software Projects

The role of project managers has become crucial to thesuccessofsoftwaredevelopmentcompanies,itisimperativeto understand customer requirements while address business needs. In this context, the present work aims to present simulation as a strategy to mitigate fails in software development effort estimation. In doing so, it will activate an outstanding project manager’s asset for risk management. This study used historical data of 181 project from a software company and the simulation package ARENA, to model and evaluation. Results point that the current method of project effort estimation - based on empirical feeling-based estimates of its employees - should be improved with the approach of continuous simulation. The modeling of this simulation provided a better understanding of efforts ranges applied in software developments on this company, and,mostimportant,theamountofrisktakenoneachtotaleffort estimative. This simulator attends the prerogatives of the project risk management area, being able to support decisions regarding schedule, resources bottlenecks and risk taken.

A novel fuzzy mechanism for risk assessment in software projects

Risk management is a vital factor for ensuring better quality software development processes. Moreover, risks are the events that could adversely affect the organization activities or the development of projects. Effective prioritization of software project risks play a significant role in determining whether the project will be successful in terms of performance characteristics or not. In this work, we develop a new hybrid fuzzy-based machine learning mechanism for performing risk assessment in software projects. This newly developed hybridized risk assessment scheme can be used to determine and rank the significant software project risks that support the decision making during the software project lifecycle. For better assessment of the software project risks, we have incorporated fuzzy decision making trial and evaluation laboratory, adaptive neuro-fuzzy inference system-based multi-criteria decision making (ANFIS MCDM) and intuitionistic fuzzy-based TODIM (IF-TODIM) approaches. More significantly, for the newly introduced ANFIS MCDM approach, the parameters of ANFIS are adjusted using a traditional crow search algorithm (CSA) which applies only a reasonable as well as small changes in variables. The main activity of CSA in ANFIS is to find the best parameter to achieve most accurate software risk estimate. Experimental validation was conducted on NASA 93 dataset having 93 software project values. The result of this method exhibits a vivid picture that provides software risk factors that are key determinant for achievement of the project performance. Experimental outcomes reveal that our proposed integrated fuzzy approaches can exhibit better and accurate performance in the assessment of software project risks compared to other existing approaches.

Development of the procedure for integrated application of scenario prediction methods

The paper proposes a procedure for the integrated application of methods for scenario analysis and prediction, represented by graphs of the «tree» type. The task on analysis of risks in software projects has been considered, the cause of which are the possible programming errors that lead to failures in the operation of systems and software. The joint use of a failure tree and a probability tree makes it possible to generate the sequences of scripts for the implementation of an adverse event, whose main cause is possible defects or errors in software or data, as well as to assess the probabilities of their realization. Such an approach allows the identification of the overall result of the influence of certain risk-forming factors (defects) on the development of possible negative consequences (failures and malfunctions) or damage to the operation of complex software systems. This makes it possible to timely identify and propose effective mechanisms to manage software risk in order to reduce and eliminate them.A procedure has been proposed for aggregating individual probabilistic expert assessments of the occurrence of a risk event. Such an approach makes it possible to obtain group expert estimates assessing the feasibility of a risk event based on the constructed system of random events into a generalized expert assessment. The probabilities of the occurrence of a risk event, thus obtained, are used when constructing a probability tree and calculating the ratios of probabilistic inference using it. Aggregation of individual expert estimates is carried out by combining them based on a mathematical apparatus of the theory of evidence and the theory of plausible and paradoxical reasoning. It was established that in order to improve quality of the results of combining it is appropriate to establish an order for combining expert evidence and apply one of the rules of conflict redistribution as a combination rule.Numerical calculations of the proposed procedure for integrated application of a failure tree and a probability tree are provided. The results obtained make it possible to run a more in-depth analysis of the examined software systems and objects, and are aimed at improving the quality and effectiveness of managing risks in software projects caused by defects in programs and data

Monitoring software risks based on integrated AHP-ANN method

Software risk management refers to systematic process for analyzing and identifying the project risks. The present paper provides a hybrid method for IT software risks identification. Software projects possess different features which increase the project failure possibilities. Therefore, the present work integrate the Artificial Neural network with the Analytic Hierarchy Process (AHP-ANN) in order to solve the problem of software project estimation in early stage. The questionnaire developed to find out the risk functional model and provide the proposed method with proper data. The results observe a major common risk in software projects is the insufficient knowledge based on different software project life cycle stages. Also, there are some other important factors in software projects such as lack of good estimation in project scheduling, poor definition of project requirements which cause human errors.

Application Development Risk Assessment Model Based on Bayesian Network

This paper describes a new risk assessment model for application development and its implementation. The model is developed using a Bayesian network and Boehm’s software risk principles. The Bayesian network is created after mapping top twenty risks in software projects with interrelationship digraph of risk area category. The probability of risk on the network is analyzed and validated using both numerical simulation and subjective probability from several experts in the field and a team of application developers. After obtaining the Bayesian network model, risk exposure is calculated using Boehm's risk principles. Finally, the implementation of the proposed model in a government institution is shown as a real case illustration.

Entailment-Based Intelligent System for Software Project Monitoring and Control

In recent years, software project managers compare actual completion of activities against the progress reports filled by project members to identify significant deviations from the estimated schedules and manage software project risks. However, quantitative measurements are limited due to the format of project documents, which are mostly natural languages. In this paper, we propose an intelligent system for software project monitoring and control by using natural language processing techniques to recognize textual entailment of progress reports to further evaluate the level of project fulfillment in a qualitative manner. Our experimental results demonstrate that the proposed method can recognize entailment from text efficiently and outperform other textual entailment approaches. Moreover, we successfully apply the textual entailment technique to project monitoring and control, which not only reduces the project cost and human's effort but also provides a basis for project managers to qualitatively evaluate the performance of each project member.

A New Project Risk Management Model based on Scrum Framework and Prince2 Methodology

With increasing competition in the software industry, software companies need to effectively manage the risks of software projects with minimal time and cost to deliver high quality products. High frequencies of warning errors and failures in software projects are indicative of human and financial costs in the software projects and teams. One of the reasons for the failure of software projects is the lack of risk management mechanism in the software development process, which can, in case of proper implementation of risk management, increase the success rate of such projects. In most projects, risk management activities are strongly confined to the adopted software methodology. Therefore, is needed a solution or model to overcome this constraint. Scrum is one of the most popular software development methodologies which has recently considered by software teams. This methodology seems not to have paid much attention to risk management. Focusing on this weakness, this research has been trying to provide a model for risk management with the participation of 52 Agile experts from six different countries using the Prince2 project management framework in Scrum methodology. The main goals of this model are to improve the coverage and appropriate risk management mechanism on software projects, increase the project’s success rate and to provide a good estimation of the required time, improve product quality and enhance quality parameters, such as the usability, flexibility, efficiency, and reliability.

Argumentation Schemes for Collaborative Debate of Requirement Risks in Software Projects

Managing risks in real-world software projects is of paramount importance. A significant class of such risks is related to the engineering of requirements, commonly involving the presentation and analysis of risk management arguments from both software engineers and clients involved in collaborative debates. In this work, drawing inspiration from argumentation theory in Artificial Intelligence, we introduce a number of “argumentation schemes” and associated “critical questions” to support such discussions. In doing so, we propose schemes related to risks due to excessive numbers of requirements; inadequate client representatives and poor understanding of client needs; incorrect, incomplete and conflicting requirements; complex and non-traceable requirements; non-stable requirements; and low quality requirements. We also discuss a case study and two experiments where the developed schemes supported the discussion of requirement risks in software projects. The overall results of these experiments indicate that our schemes are useful in the identification, proposition and analysis of requirement risks, adequately supporting debates on requirement risks.

Towards an efficient risk assessment in software projects–Fuzzy reinforcement paradigm

Effective prioritization of software project risks play an important role in determining whether the project will be successful in terms of performance characteristics or not. In this context, this study aims at hybridizing fuzzy multi criteria decision making approaches for the development of an assessment framework that can be used to efficiently identify and rank notable software project risks will aid decision making during the lifecycle of a software product. Specifically, for the assessment of project risk, we have integrated fuzzy Decision Making Trial and Evaluation Laboratory, Fuzzy Multi-Criteria Decision Making and TODIM (an acronym in Portuguese for Interactive and Multiple Attribute Decision Making) approaches. The proposed methodology is used for providing an effective rating mechanism to measure software risk factors. The effectiveness of the proposed approach results have been compared with earlier methods for the assessment of software project risks. The comparison was done based on the OMRON data set of 40 software projects. Evaluation results reveal that the integration of fuzzy approaches could be effective and as well accurate compared to existing approaches for evaluating software project risks.