 Research
 Open Access
Prediction accuracy measurements as a fitness function for software effort estimation
 Tomas Urbanek^{1}Email authorView ORCID ID profile,
 Zdenka Prokopova^{1},
 Radek Silhavy^{1} and
 Veronika Vesela^{1}
 Received: 1 October 2015
 Accepted: 24 November 2015
 Published: 15 December 2015
Abstract
This paper evaluates the usage of analytical programming and different fitness functions for software effort estimation. Analytical programming and differential evolution generate regression functions. These functions are evaluated by the fitness function which is part of differential evolution. The differential evolution requires a proper fitness function for effective optimization. The problem is in proper selection of the fitness function. Analytical programming and different fitness functions were tested to assess insight to this problem. Mean magnitude of relative error, prediction 25 %, mean squared error (MSE) and other metrics were as possible candidates for proper fitness function. The experimental results shows that means squared error performs best and therefore is recommended as a fitness function. Moreover, this work shows that analytical programming method is viable method for calibrating use case points method. All results were evaluated by standard approach: visual inspection and statistical significance.
Keywords
 Effort estimation
 Software engineering
 Use case points
 Analytical programming
 Differential evolution
 Prediction accuracy measures
Background
Effort estimation is defined as the activity of predicting the amount of effort required to complete a development of software project (Keung 2008). It is necessary to predict the effort estimation in the early stages of the software development cycle. In the best case, estimates should be calculated after a requirement analysis (Karner 1993).
Effort estimation methods can be divided into two major groups algorithmic methods and nonalgorithmic methods. Algorithmic methods carries mathematical formula, which is regression model of historical data. The most famous methods are COCOMO (Boehm 1984), FP (Atkinson and Shepperd 1994) and UCP (Karner 1993). But there is a lot of algorithmic methods. To the second category belong methods like expert judgement and analogy based methods. The most famous methods is Delphi (Rowe and Wright 1999).
The use of artificial intelligence may be a promising way to improve the accuracy of effort estimations. Accurate and consistent estimates are crucial in software project management. These estimates are used for the effective planning, monitoring and controlling of a software development cycle. Project managers may use these estimates to arrive at better management decisions. Software engineering is a complicated process because there are a lot of factors—for example, the size of the development team, the actual requirements, the programming language used, as well as other factors. These factors may have a considerably impact on the accuracy of the effort estimation process.
In this research study, the analytical programming method was used to improve the use case points method. The Use Case Points method is widely used for effort estimation in software engineering. The main benefit of this method is that it provides effort estimates at a relatively early stage in the software development cycle. Nevertheless, this method is fully dependent on the human factor since the project manager has to estimate the project parameters and set the weights. There is a low probability that two project managers will perform these estimates exactly alike. Therefore, this research uses artificial intelligence to account for this dependency on the human factor. At the same time, this method is based on straightforward computation and allows a wide range of calibration—which can be achieved by setting the weights. The combination of analytical programming and the use case points method is used to derive early and more accurate effort estimation results. Analytical programming—as a symbolic regression technique, could be used to create a new model for the use case points method. An appropriate fitness function is vital for this task (Harman and Jones 2001). The fitness function evaluates solutions and decides whether the solution is acceptable—or not, for further processing. There are a large number of prediction accuracy measurement methods for assessing the accuracy of a predictive model. Thus, in this field, one of the main obstacles is to report the accuracy correctly. MMRE or Pred(25) are mainly used for the evaluation of the statistical properties of predictive models in the software engineering field. Currently, the MMRE method is being criticised by some experts in this field—e.g., in Myrtveit et al. (2003), Shepperd et al. (2000) or Kitchenham et al. (2001); however, the method is defacto considered as a standard for reporting the suitability of a proposed model. In this study, prediction accuracy measurements will be used as fitness functions for the analytical programming.
The Sect. “Related work” of this paper summarise the related work in this field. Section “Problem statement” present the research questions for this work. Section “Experiment planning” describes the methodology used for this study. Section “Results” is devoted to the results of this work. In the next section you can see the limitations of this study. And finally, Sect. “Discussion” present discussion and conclude this paper.
The use case points method: short description

Unadjusted use case weight (UUCW)

Unadjusted actor weight (UAW)

Technical complexity factor (TCF)

Environmental complexity factor (ECF)
Unadjusted use case weight
UCP table for estimation unadjusted use case weight
Use case classification  No. of transactions  Weight 

Simple  1–3 transactions  5 
Average  4–7 transactions  10 
Complex  8 or more transactions  15 
Unadjusted actor weight
UCP table for actor classification
Actor classification  Weight 

Simple  1 
Average  2 
Complex  3 
Technical complexity factor
UCP table for technical factor specification
Factor  Description  Weight 

T1  Distributed system  2.0 
T2  Response time/performance objectives  1.0 
T3  Enduser efficiency  1.0 
T4  Internal processing complexity  1.0 
T5  Code reusability  1.0 
T6  Easy to install  0.5 
T7  Easy to use  0.5 
T8  Portability to other platforms  2.0 
T9  System maintenance  1.0 
T10  Concurrent/parallel processing  1.0 
T11  Security features  1.0 
T12  Access for third parties  1.0 
T13  End user training  1.0 
Environmental complexity factor
UCP table for environmental factor specification
Factor  Description  Weight 

E1  Familiarity with development process used  1.5 
E2  Application experience  0.5 
E3  Objectoriented experience of team  1.0 
E4  Lead analyst capability  0.5 
E5  Motivation of the team  1.0 
E6  Stability of requirements  2.0 
E7  Parttime staff  −1.0 
E8  Difficult programming language  −1.0 
Final equations
Optimization tools
In this research, we use analytical programming method with differential evolution algorithm to calibrate use case points method.
Analytical programming
The function of analytical programming can be seen in Fig. 1. In this case, the evolutionary algorithm is a differential evolution. The initial population is generated using differential evolution. This population, which must consist of natural numbers, is used for analytical programming purposes. The analytical programming then constructs the function on the basis of this population. This function is evaluated by its fitness function. If the termination condition is met, then the algorithm ends. If the condition is not met, then differential evolution creates a new population through the mutation and recombination processes. The whole process continues with the new population. At the end of the analytical programming process, it is assumed that one has a function that is the optimal solution for the given task.
Differential evolution
Differential evolution is an optimisation algorithm introduced by Storn and Price (1995). This optimisation method is an evolutionary algorithm based on population, mutation and recombination. Differential evolution is easy to implement and has only four parameters which need to be set. The parameters are: generations, NP, F and Cr. The generations parameter determines the number of generations; the NP parameter is the population size; the F parameter is the weighting factor; and the Cr parameter is the crossover probability (Storn 1996). In this research, the differential evolution is used as an analytical programming engine.
The fitness function
The fitness function is a mathematical formula that assesses the appropriateness of the solution of a given task. The selection of the appropriate fitness function is one of the most important tasks in designing an evolutionary process (Harman and Jones 2001). In the case of this study, the prediction accuracy measurements are used as fitness functions. These measurements are commonly used for the evaluation of the predictive model. It is assumed that this use of predictive accuracy measurements allows one to determine the behaviour of different fitness functions. These knowledge will be important for future research.
Related work
Some work has been done to enhance the effort estimation based on the use case points method. These enhancements cover the review and calibrating the productivity factor such as the work of Subriadi and Ningrum (2014). Another enhancement could be the construction investigation and simplification of the use case points method presented by Ochodek et al. (2011). The recent work of Silhavy et al. (2014) suggest a new approach “automatic complexity estimation based on requirements”, which is partly based on use case points method. Or using fuzzy inference system approach to improve accuracy of the use case points method (Nassif et al. 2011). Surveys such as that conducted by Kitchenham et al. (2001), have shown that MMRE measures the spread (i.e. standard deviation). Therefore, this measurement is not suitable for accuracy predictions. The same study also showed that Pred(25) is a measurement of Kurtosis. Thus far, several studies such as Burgess et al. (2001), Chavoya et al. (2013) and Chavoya et al. (2013) have tested the efficiency of using the genetic programming method for more accurate effort estimation. In 2010, Ferrucci et al. (2010) published a paper in which they used a similar principle to assess accuracy by using different fitness functions. The authors used genetic programming and the function point method for their research. Genetic programming can suffer on bloat effect and constant resolving. In this research study on the other hand, a combination of analytical programming and the use case points method were used. There is no bloat effect in analytical programming because model is built by giving the length of the model. The problem of constant resolving can be solve by metaevolution or nonlinear fitting, e.g., LevenbergMarquardt algorithm.
Problem statement

RQ1 Comparing the estimates achieved by applying analytical programming with the estimates obtained by standard use case points method equation.

RQ2 Analysing the impact of different fitness functions on the accuracy of the estimation models built with analytical programming.
Experiment planning
The proposed experiment can be seen in the Fig. 2. The process begins with a cycle that loops through the number of used fitness functions. In this case, there are ten fitness functions. Ten different seeds were used to assess the reliability of the proposed experiment. In the data preparation loop, the seed was used to split the dataset into to two distinct sets. The dataset was split into the ratio of 66 % (i.e., training set) and 33 % (i.e. testing set). The dataset is depicted in Table 5. Then, there is a third loop that runs 10 times. In this loop, the differential evolution process starts to generate an initial population. Analytical programming then uses this initial population to synthesise a new function. After that, the new function is evaluated by the one of the selected fitness functions. If the termination condition is met, one can assume that one has an optimal predictive model, and this model is then evaluated by the calculation of the least absolute deviation (LAD) on the testing set. Then, the results are saved to file for further analysis. It is necessary to note that 10 different seeds are used for every of 10 models, as well as one of the 10 fitness functions. Thus, we have a total of 10 \(\times\) 10 \(\times\) 10 solutions.
Dataset
Data used for effort estimation
ID  UUCW  UAW  TCF  ECF  Actual effort (man/h) 

1  195  12  0.780  0.780  3037 
2  80  10  0.750  0.810  1917 
3  75  6  0.900  1.050  1173 
4  130  9  0.850  0.890  742 
5  85  12  0.820  0.790  614 
6  50  9  0.850  0.880  492 
7  50  6  0.780  0.510  277 
8  305  14  0.940  1.020  3593 
9  85  12  1.030  0.800  1681 
10  130  12  0.710  0.730  1344 
11  80  9  1.050  0.950  1220 
12  70  12  0.780  0.790  720 
13  30  4  0.960  0.960  514 
14  100  15  0.900  0.910  397 
15  355  15  1.125  0.770  3684 
16  145  18  1.080  0.770  1980 
17  325  12  1.095  0.935  3950 
18  90  6  1.085  1.085  1925 
19  125  9  1.025  0.980  2175 
20  120  9  1.115  0.995  2226 
21  200  12  1.000  0.920  2640 
22  175  9  0.950  0.920  2568 
23  245  12  0.890  1.190  3042 
24  140  6  0.965  0.755  1696 
Setup of analytical programming
Parameter  Value 

Number of leafs  30 
GFSfunctions  Plus, subtract, divide, multiply, tan, sin, cos 
GFSconstants  UUCW, UAW, TCF, ECF, K 
Setup of differential evolution
Parameter  Value 

NP  40 
Generations  60 
F  0.7 
Cr  0.4 
Table 7 shows the setup of differential evolution. The best setup of differential evolution is the subject of further research.
Fitness functions
Used prediction accuracy measures
ID  Name  Equations 

1  Least absolute deviations (LAD)  \(LAD=\sum \limits _{i=1}^{n}\left y_i\hat{y}_i\right\) 
2  Mean absolute error (MAE)  \(MAE=\frac{1}{n}\sum \limits _{i=1}^{n}\left y_i\hat{y}_i\right\) 
3  Mean squared error (MSE)  \(MSE=\frac{1}{n}\sum \limits _{i=1}^{n}\left( y_i\hat{y}_i\right) ^2\) 
4  Root mean squared error (RMSE)  \(RMSE=\sqrt{\frac{1}{n}\sum \limits _{i=1}^{n}\left( y_i\hat{y}_i\right) ^2}\) 
5  Mean magnitude of relative error (MMRE)  \(MMRE=\frac{1}{n}\sum \limits _{i=1}^{n}\frac{\left y_i\hat{y}_i\right }{y_i}\) 
6  Median magnitude of relative error (MdMRE)  \(MdMRE=median\left( \frac{1}{n}\sum \limits _{i=1}^{n}\frac{\left y_i\hat{y}_i\right }{y_i}\right)\) 
7  MMRE relative to the estimate (MEMRE)  \(MEMRE\,=\,\frac{1}{n}\sum \limits _{i=1}^{n}\frac{\left y_i\hat{y}_i\right }{\hat{y}_i}\) 
8  MdMRE relative to the estimate (MdEMRE)  \(MdEMRE\,=\,median\left( \frac{1}{n}\sum \limits _{i=1}^{n}\frac{\left y_i\hat{y}_i\right }{\hat{y}_i}\right)\) 
9  R squared (\(R^2\))  \(R^2=1\frac{\sum \limits _{i\,=\,1}^{n}\left( y_i\hat{y}_i\right) ^2}{\sum \limits _{i=1}^{n}\left( y_i\bar{y}\right) ^2}\) 
10  Prediction within 25 % Pred(25)  \(Pred(25)=\frac{Number\;of\;projects,\;where\;(MRE\le 0.25)}{Number\;of\;projects}\) 
Table 8 shows the prediction accuracy measurements used. These equations were used for the learning algorithm. Standard accuracy measurements in the software engineering field—like MMRE or Pred(25) were chosen. Moreover, accuracy measurements used for general purposes—like the LAD or MSE methods were also chosen. For equations from 1 to 8; when the equation result is closer to zero, then the accuracy of the proposed model is higher. On the other hand, this condition does not apply for Eqs. 9 and 10—namely, the R squared (\(R^2\)) method and the prediction within 25 % Pred(25) method. The result of the \(R^2\) method ranges from 0 to 1, and the accuracy of the proposed model is higher when \(R^2\) is closer to 1. Likewise, the same conditions apply for Pred(25).
Results
Summary statistics for each prediction accuracy measure
Fitness function  Values are calculated by least absolute deviation (LAD) (man/h)  

Min.  1st Qu.  Median  Mean  3rd Qu.  Max.  
MMRE  1264  3556  3966  14,366  4652  1,000,000 
MdMRE  1815  3579  4109  4740  6019  12,975 
MEMRE  1844  3483  3838  14,136  4368  1,000,000 
MdEMRE  1790  3771  4514  15,481  6038  1,000,000 
MSE  1813  3268  3769  3921  4106  9613 
RMSE  1810  3284  3775  24,047  4533  1,000,000 
LAD  1816  3454  3805  12,627  4337  475,584 
MAE  1728  3599  3874  4094  4380  7842 
\(R^2\)  1810  3452  3794  24,577  4687  1,000,000 
Pred(25)  1815  3758  4107  24,774  5785  1,000,000 
Figure 5 shows the median of predicted error on testing data for Eq. 5. As can be seen the optimal productivity factor for testing dataset is between 11 and 14. The productivity factor value of 20, which is widely used, produce median error of 7469 man/h. Minimum value is 3227 man/h, if the error was set to 11.8. The median value of 3227 man/h was used as a value which need to be outperformed to have better results than from standard UCP Eq. 5.
Optimal productivity factor
Hypothesis testing for optimal productivity factor
Fitness function  p value  NULL hypothesis 

MMRE  9.29E\(\)12  False 
MdMRE  7.50E\(\)14  False 
MEMRE  2.98E\(\)11  False 
MdMRE  7.40E\(\)16  False 
MSE  5.26E\(\)08  False 
RMSE  9.56E\(\)10  False 
LAD  1.42E\(\)10  False 
MAE  4.24E\(\)10  False 
\(R^2\)  5.27E\(\)11  False 
Pred(25)  1.56E\(\)14  False 
The probability that fitness function calculate equation which is below the optimal standard UCP equation median
Fitness function  Probability (%) 

MMRE  13 
MdMRE  14 
MEMRE  19 
MdEMRE  10 
MSE  23 
RMSE  24 
LAD  20 
MAE  19 
\(R^2\)  21 
Pred(25)  9 
Table 11 show the probability that fitness function calculate equation which is below the standard UCP equation median. As can be seen on this table, the best probability is provided by RMSE fitness function. The Pred(25) fitness function show the worst result only 9 equations from 100 equations are below 3227 man/h.
Standard productivity factor
Hypothesis testing for standard productivity factor
Fitness function  p value  NULL hypothesis 

MMRE  1  True 
MdMRE  1  True 
MEMRE  1  True 
MdEMRE  1  True 
MSE  1  True 
RMSE  1  True 
LAD  1  True 
MAE  1  True 
\(R^2\)  1  True 
Pred(25)  1  True 
The probability that fitness function calculate equation which is below the standard UCP equation median
Fitness function  Probability (%) 

MMRE  97 
MdMRE  95 
MEMRE  95 
MdEMRE  81 
MSE  97 
RMSE  94 
LAD  96 
MAE  97 
\(R^2\)  94 
Pred(25)  86 
Table 13 show the probability that fitness function calculate equation which is below the standard UCP equation median. As can be seen on this table, the best probability is provided by MSE, MAE and MMRE fitness functions. The MdMRE fitness function show the worst result 81 equations from 100 equations are below 7469 man/h.
Threats to validity
It is widely recognised that several factors can bias the validity of empirical studies. Therefore, our results are not devoid of validity threats.
External validity
External validity questions whether the results can be generalized outside the specifications of a study (Milicic and Wohlin 2004). Specific measures were taken to support external validity; for example, a random sampling technique was used to draw samples from the population in order to conduct experiments. Likewise, the statistical tests used in this paper, they are also quite standard. We note that the Wilcoxon method used in this paper features prominently. We used a relatively small size dataset, which could be a significant threat to external validity. Also the employed dataset contains projects related to one context that might be characterised by some specific properties. Similarly, we do not see how a smaller or larger dataset size should yield reliable results. It is widely recognised that, SEE datasets are neither easy to find nor easy to collect. This represents an important external validity threat that can be mitigated only replicating the study on another datasets. Another validity issue to mention is that either analytical programming nor differential evolution has been exhausted via finetuning. Therefore, future work is required to exhaust all the parameters of these methods to use their best versions. Threat to external validity could be also the implementation of the analytical programming and differential evolution algorithms. Although we used standard implementations, there is considerable amount of code, which could be the threat to validity.
Internal validity
Internal validity questions to what extent the causeeffect relationship between dependent and independent variables hold (Alpaydın 2014). This paper used random sampling technique to assess methods. An alternate experimental condition would be to use Nway crossvalidation. In theory, not using crossvalidation is a threat to the validity of our results since we did not check if our results were stable across both random sampling technique and crossvalidation.
Discussion
The study started out with a goals of answering the overall research questions (RQ1) of whether analytical programming technique outperformed the standard UCP equation. This question is answered in the result section. If the UCP method is optimized, via calibrating weight or via production factor, the analytical programming method is not efficient enough to outperform standard UCP equation. The evidence can be seen in result section in Table 10. As can be seen in this table, there is no fitness function with less median value then the standard UCP equation has on the significance level 95 %. On the other hand, if the productivity factor and the whole UCP is set to default value, there is a possibility, that model built by analytical programming outperform the standard UCP equation with any of proposed fitness functions.
There is also a another question (RQ2), which must be answered. The results for answering this question is not as conclusive as we wanted to. For answering this question we need to study Tables 9, 11 and 13 from result section very carefully. From Table 9, can be seen that, MSE have the lowest median value as well as mean value and 3rd. quartile from the all of fitness functions. The maximum values, which can be seen in this table are caused by penalisation process of the evolution. With this in mind, we used median for comparison between fitness functions. The overall worst measurement result, measured by the median (MdMRE, MdEMRE), could be in its sensitivity to extreme values. The median is considered as an insensitive measure of centrality on data containing extreme values. Therefore, these measurements could be less suitable for the fitness functions. As can be seen in Tables 11 and 13, the MSE have a higher probability, that this fitness function built a model, which outperformed the standard UCP equation. If the standard productivity factor is used there is a 97 % probability, that MSE built a model more accurate then standard UCP equation. If the productivity factor is optimized there is a 23 % probability that MSE fitness function built a model, which outperformed standard UCP equation. The minimum from the whole study was calculated by MMRE fitness function. Nevertheless this minimum value was marked as a outlier as can be seen in Fig. 4.
Conclusion
The current study found that the prediction accuracy measurement, which measures the median, performs worse than those that measure the mean or total value. Surprisingly, the MMRE measurement, which has raised a lot of controversy in the effort estimation field, could be considered as an average suitable fitness function. The results also revealed that fitness functions have a reasonably influence on the calculated predictions. Analytical programming method can be seen as a viable method for effort estimation. However, this is true if and only if the UCP method is not optimized. The MSE fitness function could be seen as the best fitness function due to her statistical properties. The findings of this study have a number of important implications for future research of the using of analytical programming as an effort estimation technique. More research is required to determine the efficiency of analytical programming for this task. It would be interesting to compare Karner’s model with one of the model built by analytical programming.
Declarations
Authors' contributions
TU carried out the use of prediction accuracy measurement studies, performed the statistical analysis and drafted the manuscript.RS and ZP suggest this study, helped with design and continuously reviewing this manuscript.VV helped to draft the manuscript. All authors read and approved the final manuscript.
Acknowledgements
This study was supported by the internal Grant of Tomas Bata University in Zlin No. IGA/CebiaTech/2015/034 funded from the resources of specific university research. We are also immensely grateful to my colleagues Ales Kuncar and Andras Chernel for their comments on the earlier version of the manuscript, although any errors are our own and should not tarnish the reputations of these esteemed persons.
Competing interests
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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