Effort estimation in scrum using AI

Software effort and cost estimation are crucial parts of software project development. It determines the budget, time, and resources needed to develop a software project. The success of a software project development depends mainly on the accuracy of software effort and cost estimation. A poor estimation will impact the result, which worsens the project management. Various software effort estimation model has been introduced to resolve this problem. COnstructive COst MOdel (COCOMO) is a well-established software project estimation model; however, it lacks accuracy in effort and cost estimation, especially for current projects. Inaccuracy and complexity in the estimated effort have made it difficult to efficiently and effectively develop software, affecting the schedule, cost, and uncertain estimation directly. In this paper, Particle Swarm Optimization (PSO) is proposed as a metaheuristics optimization method to hybrid with three traditional state-of-art techniques such as Support Vector Machine (SVM), Linear Regression (LR), and Random Forest (RF) for optimizing the parameters of COCOMO models. The proposed approach is applied to the NASA software project dataset downloaded from the promise repository. Comparing the proposed approach has been made with the three traditional algorithms; however, the obtained results confirm low accuracy before hybrid with PSO. Overall, the results showed that PSOSVM on the NASA software project dataset could improve effort estimation accuracy and outperform other models.

The adjusted analogy-based software effort estimation based on similarity distances

Corroborating effort and catch from an integrated survey design for a boat-based recreational fishery in Western Australia

Since development processes for mobile applications (apps) are becoming more user centered and agile, effort and cost estimation for app development projects in large enterprises faces new challenges. In this paper, we propose a new experience-driven approach for effort and cost estimation in software development projects. A Delphi study is conducted that takes into account different perspectives in mobile app development. Recurring app features are identified and associated with effort estimates and their variations based on different roles, perspectives, and complexity levels. In order to utilize our findings, a prototypical tool is introduced that allows effort and cost estimation based on incomplete information at an early stage in a project.

Early effort estimation is important for efficiently planning the use of resources in an Information Technology (IT) project. However, limited research has been conducted on the topic of effort estimation in agile software development using artificial intelligence. This research project contributes to strengthening the use of hybrid models composed of algorithmic models and learning oriented techniques as a project-level effort estimation method in agile frameworks. Effort estimation in agile methods such as Scrum uses a story point approach that measures, using an arithmetic scale, the effort required to complete a release of the system. This project relied on labeled historical data to estimate the completion time measured in days and the total cost of a project set in Pakistani rupees (PKR). using a decision tree, random forest and AdaBoost to improve the accuracy of predictions. Models were trained using 10-fold cross-validation and the relative error was used as a comparison with literature results. The bootstrap aggregation (bagging) ensemble made of the three techniques provides the highest accuracy, and project classification also improves the estimates.

In every software development project, the software effort estimating procedure is an important process in software engineering and always critical. The consistency of effort and timeline estimation, along with several factors, determines whether a project succeeds or fails. Both academics and professionals worked on the estimation approaches in software engineering. But, all these approaches have many problems that need to be addressed. One of the most difficult aspects of software engineering is estimating effort in agile development. This study aims to provide an effort estimation method for agile software development projects. Because in software engineering, the agile method is widely used for the development of software applications. The development and usage of the agile method are described in depth in this study. The framework is configured with empirical data gathered by projects from the software industry. The test findings reveal that the estimation method has great estimation accuracy in respect of mean magnitude of relative error (MMRE) and Prediction of Error PRED (n). The suggested approach achieves more accuracy for effort estimation as compare to others.

Software development Effort Estimation (SEE) comprises predicting the most realistic amount of effort (e.g., in work hours) required to develop or maintain software based on incomplete, uncertain, and noisy input. Expert judgment is the dominant SEE strategy used in the industry. Yet, expert-based judgment can provide inaccurate effort estimates, leading to projects’ poor budget planning and cost and time overruns, negatively impacting the world economy. Large Language Models (LLMs) are good candidates to assist software professionals in effort estimation. However, their effective leveraging for SEE requires thoroughly investigating their limitations and to what extent they overlap with those of (human) software practitioners. One primary limitation of LLMs is the sensitivity of their responses to prompt changes. Similarly, empirical studies showed that changes in the request format (e.g., rephrasing) could impact (human) software professionals’ effort estimates. This paper reports the first study that replicates a series of SEE experiments, which were initially carried out with software professionals (humans) in the literature. Our study aims to investigate how LLMs’ effort estimates change due to the transition from the traditional request format (i.e., "How much effort is required to complete X?”) to the alternative request format (i.e., "How much can be completed in Y work hours?”). Our experiments involved three different LLMs (GPT-4, Gemini 1.5 Pro, Llama 3.1) and 88 software project specifications (per treatment in each experiment), resulting in 880 prompts, in total that we prepared using 704 user stories from three large-scale open-source software projects (Hyperledger Fabric, Mulesoft Mule, Spring XD). Our findings align with the original experiments conducted with software professionals: The first four experiments showed that LLMs provide lower effort estimates due to transitioning from the traditional to the alternative request format. The findings of the fifth and first experiments detected that LLMs display patterns analogous to anchoring bias, a human cognitive bias defined as the tendency to stick to the anchor (i.e., the "Y work-hours” in the alternative request format). Our findings provide crucial insights into facilitating future human-AI collaboration and prompt designs for improved effort estimation accuracy.

Background: Effort estimation is the key for efficiently managing Web projects and achieving their success. In order to correctly estimate, it is necessary to have a broad knowledge of the factors that influence effort estimation in Web projects. Aim: In this research we aim to increase the understanding of Web effort estimation by using a set of factors identified in literature along with the knowledge from experts in effort estimation. Method: We have gathered data from two different sources: (a) our previous work, in which we applied Grounded Theory procedures to identify factors that influence Web effort estimation from the point of view of Web project estimation experts; and (b) a Systematic Literature Review (SLR) extension, in which we identified factors reported in research papers. We have used the qualitative results from these sources to make comparisons and draw conclusions on factors affecting Web effort estimation. Results: We identified a total of 90 factors that influence effort estimation in Web projects. From this set, 30 factors were identified only in the qualitative study with experts in effort estimation, not being present in the SLR extension. Conclusions: By integrating the factors found in both our qualitative study with effort estimation experts and the SLR extension, we managed to create a comprehensive list of factors influencing effort estimation. Also, this set can be a starting point in the proposal of effort estimation models. Finally, the results from our comparison can be considered an indication that it is necessary to increase the employment of qualitative research to capture evidences regarding the current state of practice in Software Engineering.

One of the major activities in effective and efficient production of software projects is the precise estimation of software development effort. Estimation of the effort in primary steps of software development is one of the most important challenges in managing software projects. Some reasons for these challenges such as: discordant software projects, the complexity of the manufacturing process, special role of human and high level of obscure and unusual features of software projects can be noted. Predicting the necessary efforts to develop software using meta-heuristic optimization algorithms has made significant progressions in this field. These algorithms have the potent to be used in estimation of the effort of the software. The necessity to increase estimation precision urged the authors to survey the efficiency of some meta-heuristic optimization algorithms and their effects on the software projects. To do so, in this paper, they investigated the effect of combining various optimization algorithms such as genetic algorithm, particle swarm optimization algorithm and ant colony algorithm on different models such as COCOMO, estimation based on analogy, machine learning methods and standard estimation models. These models have employed various data sets to evaluate the results such as COCOMO, Desharnais, NASA, Kemerer, CF, DPS, ISBSG and Koten & Gary. The results of this survey can be used by researchers as a primary reference.

Respiratory and Airway Emergencies

Development effort is an undeniable part of the project management which considerably influences the success of project. Inaccurate and unreliable estimation of effort can easily lead to the failure of project. Due to the special specifications, accurate estimation of effort in the software projects is a vital management activity that must be carefully done to avoid from the unforeseen results. However numerous effort estimation methods have been proposed in this field, the accuracy of estimates is not satisfying and the attempts continue to improve the performance of estimation methods. Prior researches conducted in this area have focused on numerical and quantitative approaches and there are a few research works that investigate the root problems and issues behind the inaccurate effort estimation of software development effort. In this paper, a framework is proposed to evaluate and investigate the situation of an organization in terms of effort estimation. The proposed framework includes various indicators which cover the critical issues in field of software development effort estimation. Since the capabilities and shortages of organizations for effort estimation are not the same, the proposed indicators can lead to have a systematic approach in which the strengths and weaknesses of organizations in field of effort estimation are discovered.

The use of software engineering is vital to have maximum control of a development project, often these practices are not used when developing virtual reality projects. Currently there is little literature that provides advice to the estimation of project development efforts in virtual reality, this research provides a theoretical and empirical analysis resulting in a proposed methodology for estimating efforts. In order to have a more precise scenario, a case study for the company ENNUI in developing a virtual reality project on astro physics laboratory, was performed. The estimation of effort was conducted through the Delphi method, involving six experts in different evaluation processes, a final estimate was compared to the time it took to perform the requested requirement was made.

Time-location sampling with capture-recapture to assess specialised recreational fisheries

Effort estimation in large-scale software development: An industrial case study

Risks associated with delivery of a software project and the effort spent on managing these risks are well researched topics. Very few have included this extra effort termed as risk exposure of a project, in the software effort estimate of a project. This research proposes to improve the accuracy of software effort estimates by integrating the risk exposure with the initial effort estimate of the project. A function to calculate integrated effort estimates has been defined and evolutionary algorithms ABC, PSO and GLBPSO have been used to optimize the MMRE. The approach has been tested on two datasets collected from industry, one for waterfall projects, another for agile projects. For both the datasets, integrated effort estimates were more accurate on account of MMRE, standardized accuracy, effect size and R2, than the initial effort estimates. Evolutionary algorithms also gave the optimum weight values at which the MMRE was optimal for both the datasets. These weight values determine the contribution of risk associated with each project cost factor in the risk exposure of the project. Integrated effort estimates have been found to be more accurate, reliable, and comprehensive than the initial effort estimates. Application of evolutionary algorithms help in reducing any bias in the integrated effort estimates.