Programming Effort Estimation.

Conte, S. D.; Dunsmore, H. E.; Shen, Vincent

doi:10.21236/ada145839

Cited by 18 publications

(32 citation statements)

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“…To quantify the accuracy of the obtained estimates, we computed the summary measures MMRE, MdMRE, and Pred (0.25) (see Table 4, results using CFP A ). We can observe that no model was characterised by values satisfying the thresholds of Conte et al [7]. The best result in terms of summary measures was obtained with Bytecode kB , having MdMRE equals to 0.25 and Pred (0.25) equals to 0.54.…”

Section: Resultsmentioning

confidence: 78%

“…Furthermore, we employed some summary measures to assess the accuracy of the obtained estimations, namely MMRE, MdMRE and Pred (l) [7], which have been widely used in empirical studies similar to ours (see e.g., [16]). In the context of effort estimation, where these measures were proposed [7], l is widely set to 0.25 and a good estimation model should have a MMRE  0.25 and Pred (0.25) 0.75, that is, the mean estimation error should be less than 25 %, and at least 75 % of the estimated values should fall within 25 % of their actual values [7]. In this study we used l = 0.25.…”

Section: Correlation Test and Estimation Techniquementioning

confidence: 99%

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Investigating Functional and Code Size Measures for Mobile Applications

Ferrucci

Gravino

Salza

et al. 2015

2015 41st Euromicro Conference on Software Engineering and Advanced Applications

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In this paper we apply a measurement procedure proposed by van Heeringen and van Gorp to approximate the COSMIC size of mobile applications. We compare this procedure with the one introduced by D'Avanzo et al. We also replicate an empirical study recently carried out to assess whether the COSMIC functional size of mobile applications can be used to estimate the size of the final applications in terms of lines of code, number of bytes of the source code and bytecode. The results showed that the COSMIC functional size evaluated with van Heeringen and van Gorp's method was well correlated to all the size measures taken into account. Nevertheless, the prediction accuracy did not satisfy the evaluation criteria and turned out ot be slightly worse than the one obtained in the original study and based on the approach proposed by D'Avanzo et al.

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Section: Resultsmentioning

confidence: 78%

Section: Correlation Test and Estimation Techniquementioning

confidence: 99%

Investigating Functional and Code Size Measures for Mobile Applications

Ferrucci

Gravino

Salza

et al. 2015

2015 41st Euromicro Conference on Software Engineering and Advanced Applications

View full text Add to dashboard Cite

show abstract

“…Only two papers (G3, A1) reported accuracy levels related to the estimation techniques being investigated. These values are reported in Table 3, where we can also see that case base reasoning and UC point test effort estimation model present good accuracy values [13]. MRE values for UCP method, for all four projects in study A1, are also below 25%.…”

Section: Study Summariesmentioning

confidence: 70%

Effort Estimation in Agile Global Software Development Context

Britto

Usman

Mendes

2014

Lecture Notes in Business Information Processing

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Abstract. Both Agile Software Development (ASD) and Global Software Development (GSD) are 21 st century trends in the software industry. Many studies are reported in the literature wherein software companies have applied an agile method or practice GSD. Given that effort estimation plays a remarkable role in software project management, how do companies perform effort estimation when they use agile method in a GSD context? Based on two effort estimation Systematic Literature Reviews (SLR) -one in within the ASD context and the other in a GSD context, this paper reports a study in which we combined the results of these SLRs to report the state of the art of effort estimation in agile global software development (ASD) context.

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“…The number of records used for training, checking, and testing were 349, 100, and 50 respectively. Because the number of records is inadequate to estimate the parameters of the neuro-fuzzy estimation model, It was not possible to use the datasets below [24] The evaluation criterion used to assess the estimation accuracy are root mean square error (RMSE), and the mean magnitude (absolute) error (MME) [25]: As a comparison, Figure 3 shows the performance of a RBFN network using the same set of testing records. Table 1 shows the mean absolute error, and root mean square root for the China dataset using the adaptive neuro-fuzzy model and a radial basis function neural network (RBFN) [26].…”

Section: Software Effort Estimation Datasetsmentioning

confidence: 99%

Software Effort Estimation Using Adaptive Fuzzy-Neural Approach

Mehdi

2017

IJCATR

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Abstract-Software effort estimation is an important step in software development. It predicts the amount of effort and development time required to build a software system. It is one of the most important tasks and an accurate estimate is vital to the successful completion of the project. Building software effort estimation requires developing sound computational models. This paper investigates the use of fuzzy-neural systems in estimating software effort. A comparison is made with a radial basis neural network. Results obtained based on the China dataset indicates that a hybrid model that combine fuzzy inferencing with neural networks ability to learn from examples provided more accurate results than using neural networks alone.

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Programming Effort Estimation.

Cited by 18 publications

References 0 publications

Investigating Functional and Code Size Measures for Mobile Applications

Investigating Functional and Code Size Measures for Mobile Applications

Effort Estimation in Agile Global Software Development Context

Software Effort Estimation Using Adaptive Fuzzy-Neural Approach

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