Effort Estimation Research Articles

Scallop fishing activity characterization in Southern New England: Offshore wind demands and fisheries-dependent methods.

Managers attempt to minimize spatial use conflicts in siting of offshore wind developments, but they must rely on available data to balance biological, commercial, and recreational needs. Marine spatial planning products are only as good as the data they are built upon and fishing data present major challenges due to their confidentiality and the difficulty in isolating true fishing activity. We present a methodology to increase the spatiotemporal resolution of fishing effort and exposure estimates for Southern New England scallop fishing activity using random decision forests to perform supervised classification on AIS data, with fallback to lower resolution datasets for vessels without AIS coverage. Final predictive accuracy of the tuned random forest AIS model was 97.9%, offering improvements of 24.7, 48.6, and 50% over VTR fishing footprints, and AIS and VMS speed cutoff methods, respectively, to predict whether vessel locations correspond to fishing activity. Comparison of the AIS model with VMS and VTR fallback to the VTR fishing footprints data product demonstrated that the increased precision of the AIS point data delineated as fishing dramatically changed how fishing effort, and therefore exposure in the form of fishery landings values, is distributed spatially in Southern New England wind energy areas. This is due to how the probability of fishing is distributed across location data points in the various products, which has implications for marine spatial planning and mitigation decision-making. Therefore, multiple data products should be considered when evaluating management options, as exposure estimates may differ depending on what inputs are used. The higher resolution AIS product may offer enhanced value in understanding exposure and impacts to individual vessels, especially once wind farms are under construction or operational.

SOFTWARE COST EFFORT AND TIME ESTIMATION USING DRAGONFLY WHALE LION OPTIMIZED DEEP NEURAL NETWORK

Effective software development depends on the exact estimation of effort, time, cost, and customer satisfaction. Software project management requires an accurate evaluation of software development's effort, time, and cost, often underestimated or overestimated. So far, methodology has yet to accurately and reliably estimate the cost of software development. To overcome this issue, this paper proposed a constructive rapid application development model based on software cost effort and time estimation approach (CORADMO-based CETA) for accurate software cost estimation. The data requirements, cost drivers, constraints, and priorities are given as input to the fuzzy inference system (FIS). The processed output, such as effort, time, and cost for the nominal plan, shortest schedule plan, and least cost plan, is computed in the FIS. To reduce the effort, time, and cost, the output is optimized by dragonfly whale lion optimization (DWLO), which provides the best-estimated effort, time, and cost as an output for software development. The proposed CORADMO-based CETA model is tested in the NASA 93 dataset using MATLAB. The performance of the CORADMO-based CETA method is measured in terms of Pred (25 %), magnitude of relative error, and mean magnitude of relative error, attaining the values of 80.72 %, 87.94 %, and 98.13 %, respectively. Finally, the CORADMO-based CETA model justifies the suitability of dragonfly whale lion optimization with the proposed fuzzy logic.

ESTIMATED VERSUS ACTUAL: VARIATION IN PATROL EFFORT AND ESTIMATES OF NUMBER OF SURVEYS NEEDED FOR EFFICIENT LAW ENFORCEMENT IN PROTECTED AREAS

Law enforcement is central to the objectives of managing Protected Areas for the benefit of resident species. The study sought to appraise park patrol performance against variables needed for monitoring of the species, according to the ideologies of occupancy modelling in the Kalakpa Resource Reserve (KRR), by estimating patrol effort and comparing to estimates of survey effort needed for a precise occupancy estimation. Patrol indices were obtained from ranger patrol data and used to estimate patrol effort for rangers in the KRR. The survey effort was estimated using the values provided by the occupancy analysis of key ungulate species in KRR, for all encountered ungulates treated as a single group and four species directly sighted/heard on patrols. Encounter rates were low for ungulate species during the study period. The number of species and illegal activities observed were significantly affected by the number of staff on patrol, duration of patrols, and distance patrolled. The patrolling camp had no significant effect on the observations of species and illegal activities. Patrol effort was 190 survey occasions, with the estimated survey effort ranging from 377 to 1068 survey occasions. The estimated survey effort was significantly higher than the patrol effort and necessitated an increase in the number of patrols per unit time to improve the protection of the species in the KRR.

3Density estimation using spatial capture‐recapture analyses: application to vaccination of prairie dogs against sylvatic plague

AbstractPrairie dogs are notoriously difficult to enumerate, with previously methods including visual counts, mark‐resight, burrow counts, and catch per unit effort. Unlike those methods, spatial capture‐recapture (SCR) analyses allow for formal estimation of density along with associated estimates of uncertainty, detection probability, and the size of the average area over which an individual was detected during the study period (referred to as an activity center). Using SCR analyses, we compared density estimates as part of a field trial evaluating the effectiveness of an oral sylvatic plague vaccine in black‐tailed prairie dogs (Cynomys ludovicianus), Gunnison's prairie dogs (C. gunnisoni), white‐tailed prairie dogs (C. leucurus), and Utah prairie dogs (C. parvidens) at 11 study areas in the western United States. The study was designed as a matched pairs analysis that included 27 individual paired plots (54 plots), each consisting of a plot treated with vaccine baits and a plot treated with placebo baits. Overall, we captured >3,000 individuals each year on these plots, and recapture rates ranged from 5–87%. For black‐tailed prairie dogs, density estimates ranged from 2.7 individuals/ha (95% CI = 2.2–3.3/ha) to 77.3/ha (63.2–94.4/ha), and for Gunnison's prairie dogs, estimates ranged from 11.7/ha (10.6–12.8/ha) to 15.4/ha (14.4–16.7/ha). White‐tailed prairie dogs were at their lowest density (3.3/ha, 95% CI = 2.9–3.8/ha) during the first year of the study and their highest density (14.5/ha; 13.5–15.6/ha) during the last year of the study. Utah prairie dog density estimates ranged from a low of 4.0/ha (95% CI = 3.55–4.6/ha) to a high of 20.8/ha (16.8–25.8/ha). Best‐fitting models of prairie dog density indicated increasing patterns of density over time on most study plots, negative effects of plague, and positive effects of vaccination. Finally, we found low correlations between catch per unit effort estimates from previous published literature at these sites and our densities estimates. Spatial capture‐recapture estimates allowed us to consistently compare treatment effects across space and time, although some exceptions are noted where we observed significant movement between plots within a pair (3 pairs) and when trapping effort between plots or years was not consistent.

Feature mapping based on heterogeneous cross-company effort estimation

Feature mapping based on heterogeneous cross-company effort estimation

COMPARATIVE ANALYSIS OF METHODS AND MODELS FOR ESTIMATING SIZE AND EFFORT IN DESIGNING MOBILE APPLICATIONS

Mobile applications have long become an integral part of everyday life and have gained popularity among ordinary users, small companies, and large enterprises. However, this is a completely separate area of software. We can say that this is a whole separate ecosystem. Creating functional software for this ecosystem comes with new requirements, constraints, and characteristics that were not used in previous software metrics evaluation methods such as desktop programs or web applications. Mobile applications have new features, limitations and capabilities that are different from other software. Estimating the size and effort helps the software development team approximate the costs needed to more successfully develop a software product such as a mobile application. An early estimate of size and effort is necessary to identify resources that can help in the early stages of implementation a software project within budget and on time. The reliability of the assessment is also of great importance. Low reliability can lead to overestimation or underestimation, which can have bad consequences for any software development company, especially mobile applications. However, the suitability of methods and models for measuring size and efforts that are used for traditional software (PC applications and web applications) do not always provide sufficient reliability for mobile applications.The main goal of this article is to analyze the usage of various methods and models for estimating size and effort the development of mobile applications, including for the Android OS at the initial stages of their design. There is a brief description of the main and widespread methods and models that are used to estimate the size and effort, such as: Lines of Code (LOC), Function Points (FP), COCOMO and COCOMO 2, COSMIC, FiSMA, application of regression analysis for construction of mathematical models. The methods and models proposed by various authors are directly analyzed.

Framework to improve software effort estimation accuracy using novel ensemble rule

This investigation focuses on refining software effort estimation (SEE) to enhance project outcomes amidst the rapid evolution of the software industry. Accurate estimation is a cornerstone of project success, crucial for avoiding budget overruns and minimizing the risk of project failures. The framework proposed in this article addresses three significant issues that are critical for accurate estimation: dealing with missing or inadequate data, selecting key features, and improving the software effort model. Our proposed framework incorporates three methods: the Novel Incomplete Value Imputation Model (NIVIM), a hybrid model using Correlation-based Feature Selection with a meta-heuristic algorithm (CFS-Meta), and the Heterogeneous Ensemble Model (HEM). The combined framework synergistically enhances the robustness and accuracy of SEE by effectively handling missing data, optimizing feature selection, and integrating diverse predictive models for superior performance across varying project scenarios. The framework significantly reduces imputation and feature selection overhead, while the ensemble approach optimizes model performance through dynamic weighting and meta-learning. This results in lower mean absolute error (MAE) and reduced computational complexity, making it more effective for diverse software datasets. NIVIM is engineered to address incomplete datasets prevalent in SEE. By integrating a synthetic data methodology through a Variational Auto-Encoder (VAE), the model incorporates both contextual relevance and intrinsic project features, significantly enhancing estimation precision. Comparative analyses reveal that NIVIM surpasses existing models such as VAE, GAIN, K-NN, and MICE, achieving statistically significant improvements across six benchmark datasets, with average RMSE improvements ranging from 11.05% to 17.72% and MAE improvements from 9.62% to 21.96%. Our proposed method, CFS-Meta, balances global optimization with local search techniques, substantially enhancing predictive capabilities. The proposed CFS-Meta model was compared to single and hybrid feature selection models to assess its efficiency, demonstrating up to a 25.61% reduction in MSE. Additionally, the proposed CFS-Meta achieves a 10% (MAE) improvement against the hybrid PSO-SA model, an 11.38% (MAE) improvement compared to the Hybrid ABC-SA model, and 12.42% and 12.703% (MAE) improvements compared to the hybrid Tabu-GA and hybrid ACO-COA models, respectively. Our third method proposes an ensemble effort estimation (EEE) model that amalgamates diverse standalone models through a Dynamic Weight Adjustment-stacked combination (DWSC) rule. Tested against international benchmarks and industry datasets, the HEM method has improved the standalone model by an average of 21.8% (Pred()) and the homogeneous ensemble model by 15% (Pred()). This comprehensive methodology underscores our model’s contributions to advancing software project management (SPM) through advanced predictive modeling, setting a new benchmark for software engineering effort estimation.

SENSE: software effort estimation using novel stacking ensemble learning

SENSE: software effort estimation using novel stacking ensemble learning

Integrating adult occurrence and reproduction data to identify conservation measures for amphibians.

Monitoring programs are pivotal to establishing sound management. Due to economic, logistic, and time limitations, monitoring programs often overlook differences among life-history stages. However, species occurrence does not necessarily mean population viability, and it is unclear to what extent monitoring programs that do not consider separately adult presence and reproduction provide effective management indications. Unfortunately, collecting data on certain life stages requires high sampling effort, leading to a trade-off between model reliability and resources needed for monitoring. We collected data on presence and reproduction of amphibians by monitoring 207 waterbodies in Lombardy (northern Italy) in 2017-2022. We then used multistate occupancy models to test whether certain environmental features, namely, pond area, hydroperiod, forest cover, shade, aquatic vegetation, and predators' presence, differentially affected adult occurrence and breeding probabilities of multiple amphibian species. To assess optimal sampling efforts, we modeled the detection probabilities of adults and reproduction across multiple species. Finally, we identified the optimal monitoring strategy under different scenarios of resource availability, comparing adult-only monitoring versus joint assessment of the occurrence of adults and reproduction. In many cases, the main drivers of adult occurrence and reproduction did not coincide because most investigated ecological variables affected one life stage or the other. Forest area, for instance, increased occurrence probabilities of adults of the endemic Rana latastei but showed no effect on their reproduction probabilities. Quantitative estimates of the sampling effort showed that occurrence of adults was easier to spot in 4 out of 7 species. Multicriteria decision analyses showed that when resources were scarce, monitoring adults was the optimal strategy for those 4 species. Conversely, with more resources, monitoring both adults and reproduction emerged as the best strategy for all the considered species. Integrated monitoring of adults and reproduction is essential to comprehensively identify effective conservation measures for amphibians.

Enhancing Agile Story Point Estimation: Integrating Deep Learning, Machine Learning, and Natural Language Processing with SBERT and Gradient Boosted Trees

Advances in software engineering, particularly in Agile software development (ASD), demand innovative approaches to effort estimation due to the volatility in Agile environments. Recent trends have made the automation of story point (SP) estimation increasingly relevant, with significant potential for enhancing accuracy. This study introduces a novel model for software effort estimation (SEE) utilizing a deep learning (DL)-based sentence-BERT (SBERT) model for feature extraction combined with advanced gradient-boosted tree (GBT) algorithms. A comprehensive evaluation shows that the proposed model outperforms standard SEE and state-of-the-art models, demonstrating a mean absolute error (MAE) of 2.15 and a median absolute error (MdAE) of 1.85, representing a 12% improvement over the baseline model and an 18% improvement over the best-performing state-of-the-art model. The standardized accuracy (SA) is 93%, which is 7% higher than the next best model, across a dataset of 31,960 issues from 26 open-source Agile projects. This study contributes to software engineering by offering a more accurate and reliable decision support system for estimating project efforts.

Direct assessment of tropical tuna abundance from their associative behaviour around floating objects.

Managing populations of wild harvested species requires the ability to regularly provide accurate abundance assessments. For most marine species, changes in abundance can only be monitored indirectly, using methods reliant on harvest-based indices, with significant inherent limitations surrounding the estimation and standardization of harvest effort. Tropical tunas are some of the most exploited marine species in the world and are among several species in critical need of alternative methods for estimating abundance. Addressing this concern, we developed the Associative Behaviour-Based abundance Index (ABBI), designed to provide direct abundance estimates for animals, which exhibit an associative behaviour with aggregation sites. Its implementation in the western Indian Ocean on skipjack tuna (Katsuwonus pelamis), yellowfin tuna (Thunnus albacares) and bigeye tuna (Thunnus obesus), revealed similar trajectories in their relative abundance. The ABBI stands as a potentially promising alternative to enhance traditional tropical tuna stock assessments methods, as well as a new opportunity to assess the abundance of other wild species that display an associative behaviour with physical structures found in their natural environment.

Artificial Intelligence Implementation in Agile Project Management Addressing Challenges and Maximizing Impact

The Agile methodology, with an 80% adoption rate, often faces challenges leading to project failures. This study investigates using artificial intelligence (AI) to overcome these challenges through a systematic literature review of 44 papers. It examines AI's impact on key Agile phases: envision, speculate, explore, adapt, and close. Findings highlight AI's critical role in improving project outcomes by addressing implementation challenges. AI tools aid in risk assessment and project selection during planning, enhance effort estimation and task allocation in speculation, improve team communication and technical issue resolution in exploration, optimize systems in adaptation, and provide valuable insights in closure. The paper offers guidance on effective AI integration to enhance Agile Project Management success.

Multi-kernel support vector regression with improved moth-flame optimization algorithm for software effort estimation

In this paper, a novel Moth-Flame Optimization (MFO) algorithm, namely MFO algorithm enhanced by Multiple Improvement Strategies (MISMFO) is proposed for solving parameter optimization in Multi-Kernel Support Vector Regressor (MKSVR), and the MISMFO-MKSVR model is further employed to deal with the software effort estimation problems. In MISMFO, the logistic chaotic mapping is applied to increase initial population diversity, while the mutation and flame number phased reduction mechanisms are carried out to improve the search efficiency, as well the adaptive weight adjustment mechanism is used to accelerate convergence and balance exploration and exploitation. The MISMFO model is verified on fifteen benchmark functions and CEC 2020 test set. The results show that the MISMFO has advantages over other meta-heuristic algorithms and MFO variants in terms of convergence speed and accuracy. Additionally, the MISMFO-MKSVR model is tested by simulations on five software effort datasets and the results demonstrate that the proposed model has better performance in software effort estimation problem. The Matlab code of MISMFO can be found at https://github.com/loadstar1997/MISMFO.

Enhancing effort estimation in global software development using a unique combination of Neuro Fuzzy Logic and Deep Learning Neural Networks (NFDLNN)

ABSTRACT Effective project planning and management in the global software development landscape relies on addressing major issues like cost estimation and effort allocation. Timely estimation of software development is a critical focus in software engineering research. With the industry increasingly relying on diverse teams worldwide, accurate estimation becomes vital. Software size serves as a common measure for costs and schedules, but advanced estimation methods consider various variables, such as project purpose, personnel expertise, time and efficiency constraints, and technology requirements. Estimating software costs involve significant financial and strategic commitments, making it crucial to address complexity and versatility related to cost drivers. To achieve enhanced accuracy and convergence, we employ the cuckoo algorithm in our proposed NFDLNN (Neuro Fuzzy Logic and Deep Learning Neural Networks) model. Through extensive validation with industrial project data, using Function Point Analysis as the algorithmic models, our NFA model demonstrates high accuracy in software cost approximation, outperforming existing methods insights of MRE of 3.33, BRE of 0.13, and PI of 74.48. Our research contributes to improved project planning and decision-making processes in global software development endeavours.

Let’s measure it: An approach of high-resolution estimates of bottom fixed net fishing effort at national level

Fisheries are one of the most important food sources for human consumption whilst being amongst the most impacting and extractive activities happening within the marine environment, which makes it imperative to properly manage this activity. To improve fisheries management, the precise quantification of fishing effort is of the outmost importance. Yet, present methods for effort estimation, especially at broad scales, are hampered by difficulties in data access and usually rely on coarse effort metrics or on costly data collection for quantifying fishing effort with higher resolution. In the present work, we propose an approach of high-resolution fishing effort estimates of net fishing, as length of nets operated by a given fleet, at the national level. It relies on sampling of effort, derived from classified and easy to access vessel tracking data – AIS, and fishery dependent data – logbook and landings data. The proposed methodology combines trip-based effort estimates, derived from AIS data, as a foundation to extrapolate the total fishing effort, through the number of fishing trips linked to official landings and logbook data. It is estimated that in the years from 2014 to 2020 an average of 180 200 km of static nets (gillnets and trammel nets), which corresponds to approximately 4.5 and 210 times the lengths of the equator and the Portuguese Atlantic coastline respectively, are used in Portuguese mainland waters each year, by a fleet of slightly more than 100 vessels. The presented methodology allows to quantify and study the variation of the nominal fishing effort, at country level, with a higher resolution than what is usually used and at very low cost. We argue that such methodologies need to be developed and explored in order to have better and more comprehensive estimates of fishing effort which will contribute and improve the sustainable management of fisheries and the marine environment.

Existing datasets uncover complex interactions driving catches of a data‐poor recreational fishery in South Australia

AbstractUnderstanding drivers of effort and catch is important for sustainable management of recreational fisheries. For the Pipi (Latona deltoides) fishery in South Australia as a case study, publicly available datasets on fisher demography, market forces and internet connectivity improved understanding of fisher motivations and capabilities, identified different historical periods in the fishery and addressed temporal gaps in effort and catch estimates from on‐site surveys. Most variables were correlated with recreational effort and catch estimates from on‐site surveys and principal component analysis identified 2013–2014, 2015–2018 and 2019–2022 as distinct periods in the Pipi fishery. Results corroborated studies and anecdotal information that suggested declining availability and higher prices of local, commercially caught Pipi, and reduced imports of bivalves from Asia that previously supplied the South Australian bait market. Reduced availability and higher prices of Pipi likely motivated recreational fishers to gather their own bait to increase catch over the years. Similarly, increased internet and smartphone access and increased numbers of utility vehicles aided recreational catch efficiency of Pipi. Effective management of recreationally harvested resources requires understanding of external processes that drive fisher motivations and capabilities, particularly for stocks that are shared among fisheries. The need for improved data on fisher behaviour for management of recreational fisheries can be addressed by leveraging external information from available datasets. We recommend that the temporal gap between surveys should be less than 5 years, supplemented by analysing behavioural information collected from fishers during interviews.

Improving breathing effort estimation in mechanical ventilation via optimal experiment design

Estimation of the breathing effort and relevant lung parameters of a ventilated patient is essential to keep track of a patient’s clinical condition. The aim of this paper is to increase estimation accuracy through experiment design. The main method is an experiment design approach across multiple breaths within a linear regression framework to accurately identify the patient’s condition. Identifiability and persistence of excitation are used to formulate an estimation problem with a unique solution. Furthermore, Fisher information is used for assessing the parameters sensitivity to slight changes of the ventilator settings to improve the variance of the estimation. The estimation method is applied to simulated patients who breathe regularly but also to patients who have variable breathing patterns. A virtual experiment is conducted for both situations to generate estimation results. The results are analyzed using mathematical tools and show that uniquely estimating the lung parameters and breathing effort over multiple breaths for both regularly and variably breathing patients is possible in the presented framework. The proposed estimation method obtains clinically relevant estimates for a large set of breathing disturbances from the simulation case-study.

Concerning on different types of transthoracic echocardiography

The article discusses the evolution of transthoracic echocardiography due to the introduction into clinical practice of new technologies and methods of cardiac ultrasound and the development of pocket ultrasound devices. Often, new technologies did not replace the old ones but were used as an addition to them, which led to an increase in the duration and complexity of the examination. On the other hand, the use of portable ultrasound machines has made it possible to perform point-of-care transthoracic echocardiography, but it has been limited by solving a specific clinical problem. It became clear that different clinical situations require examinations that differ in complexity, labor intensity, levels of experience, and training of the doctors. Various types, features, and place in the clinical practice of transthoracic echocardiography are presented. The necessity of changing approaches to both accounting for effort estimates depending on the protocols and volumes of examinations and to planning the work of ultrasound and functional diagnostics departments is substantiated. Ways to solve problems that hinder the introduction of targeted, including focused, examinations into routine clinical practice are proposed.

Particle Swarm Optimization-based Linear Regression for Software Effort Estimation

In the context of software effort estimation, this study investigates the use of Particle Swarm Optimization (PSO)-based Linear Regression to improve estimation accuracy. The main problem faced is the limitations of standard Linear Regression models in accurately estimating the effort required for software development projects. This research aims to improve the quality of estimation of software efforts to optimize resource management and project schedules. The method used was the integration of PSOs in Linear Regression, which was evaluated using three different COCOMO datasets. Experimental results show that LR+PSO models consistently outperform standard Linear Regression with lower MAE, MSE, and RMSE, as well as higher R-squared. In conclusion, integrating PSOs in Linear Regression effectively improves the estimation accuracy of software efforts, demonstrating great potential for improving estimation quality in software project management practices.

Forecasting the software engineering model’s effort estimation using constructive cost estimation models

Forecasting the software engineering model’s effort estimation using constructive cost estimation models