Realistic Effort Research Articles

An improved analogy-rule based software effort estimation using HTRR-RNN in software project management

To complete a software project, the most realistic effort required is predicted by the process called Software Effort Estimation (SEE). Effort Estimation (EE) in software development is considered a complicated task owing to the variability of variables. Thus, numerous Software Development EE (SDEE) models have been developed; nevertheless, these models' accuracy rates are not good enough, and the variations among software functionalities were not focused for SEE. Therefore, by considering it as the research gap in Software Project Management (SPM), an enhanced analogy-rule-centric SEE utilizing Hyperbolic Tangent Recurrent Radial − Recurrent Neural Networks (HTRR-RNN) algorithm is proposed in this work. Initially, regarding the project size, the projects are partitioned. Next, the highly vital features are extracted as of the separated projects. By employing Levy Flight-Rock Hyraxes Swarm (LH-RHS) optimization, the selective quantity of features is chosen from the extracted features. By deploying the Mamdani Method − Fuzzy (MM-Fuzzy) algorithm, the rules are created regarding the selected features. Lastly, the number of efforts needed for the software development process is determined by the classification algorithm regarding the rules being created. COCOMO81, MAXWELL, and China are the openly accessible datasets utilized in this methodology. Then, the proposed method’s results are contrasted with the prevailing algorithms. As per the experimental results, the proposed methodology performs SEE with a better accuracy rate; thus, it surpassed the other existing methodologies.

Getting in touch with metadata: a DDI subset for FAIR metadata production in clinical psychology

To address metadata with researchers it is important to use models that include familiar domain concepts. In the Social Sciences, the DDI is a well-accepted source of such domain concepts. To create FAIR data and metadata, we need to establish a compact set of DDI elements that fit the requirements in projects and are likely to be adopted by researchers inexperienced with metadata creation. Over time, we have engaged in interviews and data description sessions with research groups in the Social Sciences, identifying a manageable DDI subset. A recent Clinical Psychology project, TOGETHER, dealing with risk assessment for hereditary cancer, considered the inclusion of a DDI subset for the production of metadata that are timely and interoperable with data publication initiatives in the same domain. Taking a DDI subset identified by the data curators, we make a preliminary assessment of its use as a realistic effort on the part of researchers, taking into consideration the metadata created in two data description sessions, the effort involved, and overall metadata quality. A follow-up questionnaire was used to assess the perspectives of researchers regarding data description.

Realistic effort action plans (REAP) for exercise among underactive and inactive university students: A randomized trial

Objective: The efficacy of effort appraisal exercise action plans was tested among underactive and inactive university students (N = 221). Methods: Students were randomized across three conditions (information, action planning, or realistic effort action planning (REAP)) and participated in psychoeducational small-group sessions. Students returned after a three-week pedometer tracking period and at two and six months to assess self-reported exercise. Results: Greater three-week step averages were observed for the action planning and REAP conditions compared to the information condition. The information condition showed small-sized exercise increases at two and six months (d = .26, d = .35, ps < .05); the action planning condition showed a small-sized increase at six months (d = . 36, p < 05); and the REAP condition showed medium-sized increases at two and six months (d = .40, d = . 46, ps < .05). Conclusions: The findings provide initial evidence showing exercise action plans for college students might be improved with explicit appraisals of prior effort and persistence.

Determining an effective sampling method for eDNA metabarcoding: a case study for fish biodiversity monitoring in a small, natural river

In recent years, biodiversity loss has become one of the most serious environmental issues worldwide, especially in aquatic ecosystems. To avoid diversity loss, it is necessary to monitor biological communities, and environmental DNA (eDNA) metabarcoding has been developed as a rapid, noninvasive, and cost-effective method for aquatic biodiversity monitoring. Although this method has been applied to various environments and taxa, a detailed assessment of the efficient sampling methods for monitoring is still required. In this study, we explored eDNA metabarcoding sampling methods for fish at a single site to maximize the number of detected species using realistic effort in a natural, small river. We considered the following three parameters: sample type (water or sediment), sample position at a site (right and left shore and center of the river), and water volume (10–4000 mL). The results suggested that the number of detected species from sedimentary eDNA was equivalent to that from aqueous eDNA, although the species composition was different. The number of detected species could be saturated by collecting a 1000 mL water sample, regardless of sampling position within a survey site. However, sedimentary eDNA showed a spatially heterogeneous species composition between sampling positions within a survey site despite the short distance (5 m) between positions, without apparent differences in physical properties such as velocity and sediment particle distribution. By completing eDNA biodiversity monitoring of fish with 1000 mL water samples across the whole river, we detected more fish species than in previous traditional surveys conducted at the same sites. Thus, the aqueous eDNA metabarcoding method is as efficient as traditional surveys, while sedimentary eDNA metabarcoding could complement the results of aqueous eDNA metabarcoding.

NOVEL APPROACH FOR COST ESTIMATION OPTIMIZATION BASE FEATURE SELECTION AND CLASSIFICATION

Software development effort estimation is the process of predicting the most realistic effort required to develop or maintain software. It is important to develop estimation models and appropriate techniques to avoid losses caused by poor estimation. IN text mining is use for understand user requirement, so understanding features optimize by ant colony optimization, it reduce the overlapping of features and then learn by generative classifier using Naïve Bayes.

Software Cost Estimation Approaches: A Survey

The software cost estimation aims to predict the most realistic effort that is required to finish a software project and so it is critical to the success of a software project management. A Software Cost Estimation affects nearly all management activities, including project bidding, resource allocation and project planning. It is affected by a number of factors, such as implementation efficiency, as well as how much the various reviews and studies completed prior to the software development stage cost. Accurate cost estimation will help us to complete the project on time and within budget. Accurate estimation is important because it has led to extensive research into the methods of software cost estimation. Some important software cost estimation methods have been studied in this research work. In addition, we have set out own criteria, which has been used to compare all the different selected methods. We have also given a score for each evaluation criteria, so that we can compare the different methods numerically for cost estimation. Our observations have shown that it is best to use a number of different estimating techniques or cost models, and then compare the results before determining the reasons for any of the large variations. None of the methods are necessarily better or worse than the others. We found, in fact, that their strengths and weaknesses often complement each other. Therefore, the main conclusion is that there is no one single technique that is best for every situation, and the results of a number of different approaches need to be carefully considered to discover what is the most likely to produce estimates that are realistic.

Ecological risk and accessibility analysis to assess the impact of roads under Habitats Directive

We propose a method for the appropriate assessment of adverse effects of roads in compliance with the European Union Habitats Directive. The method incorporates an analysis of ecological risk of edge effects by the proposed roads with the related increase in accessibility. The method was tested on 30 km of planned forest roads inside an 8,000-ha reserve included in two Natura 2000 sites. As a result, the cumulative effect of 19 road segments was judged as not significantly affecting the integrity of the sites, although they made accessible an extra 314 ha. On the basis of the accessibility calculation, 20 ha of land were set aside from forest exploitation as a mandatory mitigation measure. The method objectively determined the cumulative adverse effects, enabled comparison of plan revisions and alternatives and proved to measure direct and indirect significant effects with a realistic effort in terms of field survey and geographic information system processing.

Unit effects in software project effort estimation: Work-hours gives lower effort estimates than workdays

Software development effort estimates are typically expert judgment-based and too low to reflect the actual use of effort. Our goal is to understand how the choice of effort unit affects expert judgement-based effort estimates, and to use this knowledge to increase the realism of effort estimates. We conducted two experiments where the software professionals were randomly instructed to estimate the effort of the same projects in work-hours or in workdays. In both experiment, the software professionals estimating in work-hours had much lower estimates (on average 33%–59% lower) than those estimating in workdays. We argue that the unitosity effect—i.e., that we tend to infer information about the quantity from the choice of unit—is the main explanation for the large difference in effort estimates. A practical implication of the unit effect is that, in contexts where there is a tendency toward effort under estimation, the instruction to estimate in higher granularity effort units, such as workdays instead of work-hours, is likely to lead to more realistic effort estimates.

Optimizing Basic COCOMO Model Using Simplified Genetic Algorithm

The estimation of software effort is an essential and crucial activity for the software development life cycle. In recent years, many researchers and software industries have given significant attention on the estimation of software effort. In industry, effort is used for planning, budgeting and development time calculation. Therefore a realistic effort estimation is required. Many researchers have proposed various models for software effort estimation, such as statistical models, algorithmic models, machine learning based models and nature inspired models in the past. In this research paper, a simplified genetic algorithm based model is proposed. A simplified genetic algorithm is used for optimizing the parameters of the basic COCOMO model. The proposed approach is applied on NASA software project dataset. Experimental results show better realistic estimation over the basic COCOMO.

Integrating uncertainty in software effort estimation using Bootstrap based Neural Networks

Software effort estimation is a crucial task in the software project management. It is the basis for subsequent planning, control, and decision-making. Reliable effort estimation is difficult to achieve, especially because of the inherent uncertainty arising from the noise in the dataset used for model elaboration and from the model limitations. This research paper proposes a software effort estimation method that provides realistic effort estimates by taking into account uncertainty in the effort estimation process. To this end, an approach to introducing uncertainty in Neural Network based effort estimation model is presented. For this purpose, bootstrap resampling technique is deployed. The proposed method generates a probability distribution of effort estimates from which the Prediction Interval associated to a confidence level can be computed. This is considered to be a reasonable representation of reality, thus helping project managers to make well-founded decisions. The proposed method has been applied on a dataset from International Software Benchmarking Standards Group and has shown better results compared to traditional effort estimation based on linear regression.

Ranking and Clustering Software Cost Estimation Models through a Multiple Comparisons Algorithm

Software Cost Estimation can be described as the process of predicting the most realistic effort required to complete a software project. Due to the strong relationship of accurate effort estimations with many crucial project management activities, the research community has been focused on the development and application of a vast variety of methods and models trying to improve the estimation procedure. From the diversity of methods emerged the need for comparisons to determine the best model. However, the inconsistent results brought to light significant doubts and uncertainty about the appropriateness of the comparison process in experimental studies. Overall, there exist several potential sources of bias that have to be considered in order to reinforce the confidence of experiments. In this paper, we propose a statistical framework based on a multiple comparisons algorithm in order to rank several cost estimation models, identifying those which have significant differences in accuracy, and clustering them in nonoverlapping groups. The proposed framework is applied in a large-scale setup of comparing 11 prediction models over six datasets. The results illustrate the benefits and the significant information obtained through the systematic comparison of alternative methods.

Contrasting ideal and realistic conditions as a means to improve judgment-based software development effort estimation

Context The effort estimates of software development work are on average too low. A possible reason for this tendency is that software developers, perhaps unconsciously, assume ideal conditions when they estimate the most likely use of effort. In this article, we propose and evaluate a two-step estimation process that may induce more awareness of the difference between idealistic and realistic conditions and as a consequence more realistic effort estimates. The proposed process differs from traditional judgment-based estimation processes in that it starts with an effort estimation that assumes ideal conditions before the most likely use of effort is estimated. Objective The objective of the paper is to examine the potential of the proposed method to induce more realism in the judgment-based estimates of work effort. Method Three experiments with software professionals as participants were completed. In all three experiments there was one group of participants which followed the proposed and another group which followed the traditional estimation process. In one of the experiments there was an additional group which started with a probabilistically defined estimate of minimum effort before estimating the most likely effort. Results We found, in all three experiments, that estimation of most likely effort seems to assume rather idealistic assumptions and that the use of the proposed process seems to yield more realistic effort estimates. In contrast, starting with an estimate of the minimum effort, rather than an estimate based on ideal conditions, did not have the same positive effect on the subsequent estimate of the most likely effort. Conclusion The empirical results from our studies together with similar results from other domains suggest that the proposed estimation process is promising for the improvement of the realism of software development effort estimates.

Competition and the Collective Management of Copyright

Discussions of the collective management of copyright tend to celebrate their subject. Much of the work collected in this volume focuses upon the significant economic value created by collective management organizations (“CMOs”), as well as the practical difficulties presented by any realistic effort to unlock that value. I have been assigned a different role to play. My task is to explain one downside of CMOs, namely the risk they pose to competition, and hence the limitations that antitrust law places upon their activities. These limits are familiar to many symposium participants. After all, two of the leading CMOs in the United States, ASCAP and BMI, have operated under an antitrust consent decree for the past sixty years. No article PDF is available.

Preliminary study of sequence effects in judgment-based software development work-effort estimation

Software development effort estimates are often inaccurate. This study investigates to what degree and why the sequence in which we estimate software work affect the effort estimates. The results may be used to improve judgment-based software development effort estimation processes. Two controlled experiments were conducted. In the first experiment, software professionals were randomly allocated to the groups SMALL and LARGE. First, those in group SMALL estimated the most likely effort required to complete a small software development task and those in group LARGE the effort of a larger task. Then both groups estimated the effort of the same medium-sized task. The first estimate had a large impact on the subsequent. The second experiment aimed at a better understanding of the nature of sequence effects in effort estimation. This experiment suggests that it is the experience and knowledge activated in the previous task that matter, not the estimated value itself. In conclusion, more awareness of the importance of the estimation sequence may lead to more realistic effort estimates. In particular, it may be useful to avoid estimation of simple and small tasks just before larger and more complex tasks in situations where over-optimism is frequent.

The HUPO Proteomics Standards Initiative - Overcoming the Fragmentation of Proteomics Data

Proteomics is a key field of modern biomolecular research, with many small and large scale efforts producing a wealth of proteomics data. However, the vast majority of this data is never exploited to its full potential. Even in publicly funded projects, often the raw data generated in a specific context is analysed, conclusions are drawn and published, but little attention is paid to systematic documentation, archiving, and public access to the data supporting the scientific results. It is often difficult to validate the results stated in a particular publication, and even simple global questions like "In which cellular contexts has my protein of interest been observed?" can currently not be answered with realistic effort, due to a lack of standardised reporting and collection of proteomics data. The Proteomics Standards Initiative (PSI), a work group of the Human Proteome Organisation (HUPO), defines community standards for data representation in proteomics to facilitate systematic data capture, comparison, exchange and verification. In this article we provide an overview of PSI organisational structure, activities, and current results, as well as ways to get involved in the broad-based, open PSI process.

Fuzzy geometric programming approach to a fuzzy machining economics model

Machining economics is an important function of the process planning activity for manufacturing products with high quality and low cost. The machining economics model usually contains a highly non-linear objective function and equations that could be formulated as a geometric programming problem. The paper develops a solution method for deriving the fuzzy objective value of the fuzzy machining economics problem when some of the parameters in the problem are fuzzy numbers. A pair of geometric programs is formulated to calculate the lower and upper bounds of the unit production cost at possibility level α. With the ability to calculate the fuzzy objective value developed, it might help lead to a more realistic modelling effort. The developed methodology can also be applied to other engineering design problems with fuzzy numbers.

Impact of effort estimates on software project work

This paper presents results from two case studies and two experiments on how effort estimates impact software project work. The studies indicate that a meaningful interpretation of effort estimation accuracy requires knowledge about the size and nature of the impact of the effort estimates on the software work. For example, we found that projects with high priority on costs and incomplete requirements specifications were prone to adjust the work to fit the estimate when the estimates were too optimistic, while too optimistic estimates led to effort overruns for projects with high priority on quality and well specified requirements. Two hypotheses were derived from the case studies and tested experimentally. The experiments indicate that: (1) effort estimates can be strongly impacted by anchor values, e.g. early indications on the required effort. This impact is present even when the estimators are told that the anchor values are irrelevant as estimation information; (2) too optimistic effort estimates lead to less use of effort and more errors compared with more realistic effort estimates on programming tasks.

Reports Will Urge Overhaul and Delays to NASA's Mars Missions

SPACE SCIENCEThe failure of two Mars missions last year has shattered NASA's ambitious plan to launch a series of orbiting spacecraft and landers every 2 years, culminating in the return of a soil and rock sample to Earth in 2008. Now NASA is rushing to come up with a less daring but more realistic effort--one that agency officials warn will cost more and take longer to accomplish. Science has learned that the revised plan may delay some science missions, such as the lander being readied for a 2001 launch, and cost $300 million to $500 million more than NASA originally estimated.

Phenomenological modeling of the non-linear electro-mechanical coupling in ferroelectrics

Our objective is the construction of a phenomenological model of ferroelectricity for general electro-mechanical loading histories, which is simple enough to be implemented in an FE-code with realistic effort. In this paper we motivate and verify the one-dimensional formulation of such a model. It relies upon introducing the remanent polarization and the remanent strain as internal variables besides stress, strain, electric field and polarization. The internal variables are governed by ordinary differential equations. Each of these evolution equations is subjected to two loading conditions of different nature. The first one indicates the onset of changes of the remanent quantities by domain switching, while the second one characterizes the saturation value of a remanent quantity corresponding to a totally switched domain structure. Polarization induced anisotropy is taken into account as far as it seemed necessary. For simplicity, no rate effects are included. The model response to uniaxial electro-mechanical loading histories will be discussed in comparison to known experimental results. By means of a bilinear approximation the following characteristic phenomena of macroscopic ferroelectricity are represented : dielectric hysteresis, polarization induced piezoelectricity, butterfly hysteresis, ferroelastic hysteresis, mechanical depolarization.

Product management challenge to software change process: preliminary results from three SMEs experiment

At some point all small software companies face a situation where they simply have to start to define and improve their software processes. The difficulties they normally have to cope with are the limited resources and the lack of knowledge of how to do a plan and to carry out a successful and realistic process improvement effort. Surprisingly, often the main problem in small companies is that some software sub-processes have not been defined and implemented at all. In this kind of situation good results can sometimes be achieved by benchmarking the solutions of other companies and then developing a solution of one’s own. Another important topic when planning process improvement effort is the selection of the improvement key-area and finding enough background information about it. This paper introduces an example case of improving software product management (SPM) process maturity in three SMEs. © 1997 John Wiley & Sons, Ltd.