# The Effect of Green Software: A Study of Impact Factors on the Correctness of Software

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

- Step 1: Among the different AI techniques, we choose the four methods LR, DT, MLP and SVM.
- These four methods LR, DT, MLP and SVM are the basis to construct the model.
- Finally, the model is built through the training model and then it will be capable to predict/classify.

#### 2.1. Data Description

#### 2.2. AI Methods

#### 2.2.1. Logistic Regression

#### 2.2.2. Decision Trees

- Decision nodes: Usually represented by circles. From the circles appear the arcs with the diverse decisions.
- Leaf nodes: Represented by squares.

#### 2.2.3. ANN—Multilayer Perceptron

- An input layer receives external inputs.
- One or more hidden layers transform the inputs into something useful for the output layer.
- Finally, an output layer generates the classification results.

- p is the number of inputs,
- ${\nu}_{j}$ is the linear combination of inputs ${x}_{1},{x}_{2},...,{x}_{p}$,
- the threshold ${\theta}_{j}$, ${w}_{ji}$ is the connection weight between the input ${x}_{i}$ and the neuron j,
- and ${f}_{j}$ is the activation function of the ${j}_{th}$ neuron, and ${y}_{j}$ is the output.

- All the weight vectors w are initialized with small random values from a pseudorandom sequence generator.
- Three basic steps are repeated until the convergence is achieved, that is, the error E is below a preset value.
- The weight vectors ${w}_{i}$ are updated by$$w(t+1)=w\left(t\right)+\Delta w\left(t\right),$$$$\Delta w\left(t\right)=-\eta \partial E\left(t\right)/\partial w.$$
- Compute the error E(t + 1),

where t is the iteration number, w is the weight vector, and $\eta $ is the learning rate.

#### 2.2.4. Support Vector Machines

#### 2.2.5. Evaluation

- n is the size of the dataset S,
- ${x}_{i}$ is the example of S,
- ${y}_{i}$ is the target of ${x}_{i}$,
- and ${S}_{i}$ is the probable target of ${x}_{i}$ by the classifier function I.

## 3. Results

## 4. Discussion

#### 4.1. Academic Findings

#### 4.2. Educational Data Mining Findings

#### 4.3. Sustainability Findings

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 2.**The architecture of the MLP network consists of input layer, hidden layer and output layer. The input layer represents the input data (the input data set is described in Section 2.1). The output layer represents the classification result and it contains as many outputs as the number of classes in a particular problem. The hidden layer is calculated in the experimentation.

**Figure 4.**Final mark of the practice of each collaborative group. Execution times are relative to the best time for each year.

**Figure 5.**Final mark of the practice of each collaborative group with respect to memory consumption of the last submission of the group to the last problem (which contains the whole practice). Memory is relative to the best memory usage for each year.

**Figure 6.**Two fragments of sample of code showing good (left) and bad (right) programming practices.

Variable Name | Data Type | Description |
---|---|---|

NUMBER_ENROLL | Number | Number of times the student has been previously enrolled in ADS |

NUMBER_FAIL | Number | Number of times the student has failed ADS in previous enrollments (each enrollment entitles a maximum of three evaluations) |

GENDER | Binary | Student’s gender |

ABSENT | Number | Number of absences of the student to the weekly lectures |

M_T1..M_T4 | Number | Mark of the student in the activities of topics T1, T2, T3 and T4 |

PRACTICE | Number | Mark of the student practices |

THEORY | Number | Mark of the student theory (mean of marks of T1...T4) |

GROUP_SIZE | Binary | Whether the project is done individually or in a group of two |

TURN | Text | Turn in with the student is studying (morning or afternoon turns) |

EXERCISE | Number | Number of the exercise of the submission (numbers 0XX are basic input/output problems, 2XX are related to topic 2, and 3XX to topic 3) |

LANGUAGE | Text | Programming language used (C or C++) |

PROG_SIZE | Number | Size of the program submitted |

RUNNING TIME | Number | Execution time consumed by the program in the online judge tests (in ms) |

MEMORY | Number | Memory used by the program in the online judge tests (in bytes) |

SUBMIT_TIME | Number | Time when the submission is done |

WEEK_DAY | Number | Day of the week when the submission is done, from Sunday (0) to Saturday (6) |

DAYS_DEADLINE | Number | Number of days until the deadline of the activity |

GRADE | Number | Final mark of the student |

YEAR | Number | Academic course |

EVALUATION | Text | Evaluation of the program done by the online judge (accepted or not accepted). This is the output variable |

DT | MLP | SVM | LR | |||||
---|---|---|---|---|---|---|---|---|

Actual | Predicted | Predicted | Predicted | Predicted | ||||

Collaborative programming activities | ||||||||

A | R | A | R | A | R | A | R | |

A | 942 | 304 | 809 | 437 | 916 | 330 | 894 | 352 |

R | 351 | 734 | 487 | 598 | 538 | 547 | 493 | 592 |

Individual programming activities | ||||||||

A | R | A | R | A | R | A | R | |

A | 1034 | 492 | 769 | 757 | 359 | 1167 | 511 | 1015 |

R | 461 | 1708 | 716 | 1453 | 279 | 1890 | 427 | 1742 |

DT | MLP | SVM | LR | ||
---|---|---|---|---|---|

Collaborative programming activities | |||||

Class. acc. (%) | $\frac{TP+TN}{TP+FP+FN+TN}}\times 100\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}$ | 71.9% | 60.4% | 62.8% | 63.7% |

Sensitivity (%) | $\frac{TP}{TP+FN}}\times 100\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}$ | 75.6% | 64.9% | 73.5% | 71.7% |

Specificity (%) | $\frac{TN}{FP+TN}}\times 100\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}$ | 67.6% | 55.1% | 50.4% | 54.6% |

Pos. pred. (%) | $\frac{TP}{TP+FP}}\times 100\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}$ | 72.9% | 62.4% | 63.0% | 64.5% |

Neg. pred. (%) | $\frac{TN}{FN+TN}}\times 100\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}$ | 70.7% | 57.8% | 62.4% | 62.7% |

Individual programming activities | |||||

Class. acc. (%) | $\frac{TP+TN}{TP+FP+FN+TN}}\times 100\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}$ | 74.2% | 60.1% | 60.9% | 61.0% |

Sensitivity (%) | $\frac{TP}{TP+FN}}\times 100\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}$ | 67.8% | 50.4% | 23.5% | 33.5% |

Specificity (%) | $\frac{TN}{FP+TN}}\times 100\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}$ | 78.7% | 67.0% | 87.1% | 80.3% |

Pos. pred. (%) | $\frac{TP}{TP+FP}}\times 100\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}$ | 69.2% | 51.8% | 56.3% | 54.5% |

Neg. pred. (%) | $\frac{TN}{FN+TN}}\times 100\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}$ | 77.6% | 65.7% | 61.8% | 63.2% |

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## Share and Cite

**MDPI and ACS Style**

Gil, D.; Fernández-Alemán, J.L.; Trujillo, J.; García-Mateos, G.; Luján-Mora, S.; Toval, A.
The Effect of Green Software: A Study of Impact Factors on the Correctness of Software. *Sustainability* **2018**, *10*, 3471.
https://doi.org/10.3390/su10103471

**AMA Style**

Gil D, Fernández-Alemán JL, Trujillo J, García-Mateos G, Luján-Mora S, Toval A.
The Effect of Green Software: A Study of Impact Factors on the Correctness of Software. *Sustainability*. 2018; 10(10):3471.
https://doi.org/10.3390/su10103471

**Chicago/Turabian Style**

Gil, David, Jose Luis Fernández-Alemán, Juan Trujillo, Ginés García-Mateos, Sergio Luján-Mora, and Ambrosio Toval.
2018. "The Effect of Green Software: A Study of Impact Factors on the Correctness of Software" *Sustainability* 10, no. 10: 3471.
https://doi.org/10.3390/su10103471