The Effect of Green Software: A Study of Impact Factors on the Correctness of Software
Abstract
: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,
- is the linear combination of inputs ,
- the threshold , is the connection weight between the input and the neuron j,
- and is the activation function of the neuron, and 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 are updated by
- Compute the error E(t + 1),
where t is the iteration number, w is the weight vector, and is the learning rate.
2.2.4. Support Vector Machines
2.2.5. Evaluation
- n is the size of the dataset S,
- is the example of S,
- is the target of ,
- and is the probable target of 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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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. (%) | 71.9% | 60.4% | 62.8% | 63.7% | |
Sensitivity (%) | 75.6% | 64.9% | 73.5% | 71.7% | |
Specificity (%) | 67.6% | 55.1% | 50.4% | 54.6% | |
Pos. pred. (%) | 72.9% | 62.4% | 63.0% | 64.5% | |
Neg. pred. (%) | 70.7% | 57.8% | 62.4% | 62.7% | |
Individual programming activities | |||||
Class. acc. (%) | 74.2% | 60.1% | 60.9% | 61.0% | |
Sensitivity (%) | 67.8% | 50.4% | 23.5% | 33.5% | |
Specificity (%) | 78.7% | 67.0% | 87.1% | 80.3% | |
Pos. pred. (%) | 69.2% | 51.8% | 56.3% | 54.5% | |
Neg. pred. (%) | 77.6% | 65.7% | 61.8% | 63.2% |
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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
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 StyleGil, 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