An estimate of necessary effort in the development of software projects

International Workshop on Intelligent Technologies for Software Engineering (WITSE'04). 19th IEEE International Conference on Automated Software Engineering (Linz, Austria, September 20th - 25th, 2004)The estimated of the effort in the development of software projects has already been studied in the field of software engineering. For this purpose different ways of measurement such as Unes of code and function points, generally addressed to relate software size with project cost (effort) have been used. In this work we are presenting a research project that deals with this field, us'mg machine learning techniques to predict the software project cost. Several public set of data are used. The analysed sets of data only relate the effort invested in the development of software projects and the size of the resultant code. For this reason, we can say that the data used are poor. Despite that, the results obtained are good, because they improve the ones obtained in previous analyses. In order to get results closer to reality we should find data sets of a bigger size that take into account more variables, thus offering more possibilities to obtain solutions in a more efficient way.Publicad

Hybrid intelligent model for software maintenance prediction

Maintenance is an important activity in the software life cycle. No software product can do without undergoing the process of maintenance. Estimating a software’s maintainability effort and cost is not an easy task considering the various factors that influence the proposed measurement. Hence, Artificial Intelligence (AI) techniques have been used extensively to find optimized and more accurate maintenance estimations. In this paper, we propose an Evolutionary Neural Network (NN) model to predict software maintainability. The proposed model is based on a hybrid intelligent technique wherein a neural network is trained for prediction and a genetic algorithm (GA) implementation is used for evolving the neural network topology until an optimal topology is reached. The model was applied on a popular open source program, namely, Android. The results are very promising, where the correlation between actual and predicted points reaches 0.9

Pemetaan Secara Sistematis Pada Metrik Kualitas Perangkat Lunak

. Software quality assurance is one method to increase quality of software. Improvement of software quality can be measured with software quality metric. Software quality metrics are part of software quality measurement model. Currently software quality models have a very diverse types, so that software quality metrics become increasingly diverse. The various types of metrics to measure the quality of software create proper metrics selection issues to fit the desired quality measurement parameters. Another problem is the validation need to be performed on these metrics in order to obtain objective and valid results. In this paper, a systematic mapping of the software quality metric is conducted in the last nine years. This paper brings up issues in software quality metrics that can be used by other researchers. Furthermore, current trends are introduced and discussed

Predicting Fault-prone Software Module Using Data Mining Technique and Fuzzy Logic

This paper discusses a new model towards reliability and quality improvement of software systems by predicting fault-prone module before testing. Model utilizes the classification capability of data mining techniques and knowledge stored in software metrics to classify the software module as fault-prone or not fault-prone. A decision tree is constructed using ID3 algorithm for existing project data in order to gain information for the purpose of decision making whether a particular module id fault-prone or not. The gained information is converted into fuzzy rules and integrated with fuzzy inference system to predict fault-prone or not fault-prone software module for target data. The model is also able to predict fault-proneness degree of faulty module. The goal is to help software manager to concentrate their testing efforts to fault-prone modules in order to improve the reliability and quality of the software system. We used NASA projects data set from the PROMOSE repository to validate the predictive accuracy of the model

A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment

Software quality is an important research area and has gain considerable attention from software engineering community in identification of priority quality attributes in software development process. This thesis describes original research in the field of software quality model by presenting a Feature Ranking Algorithm (FRA) for Pragmatic Quality Factor (PQF) model. The proposed algorithm is able to improve the weaknesses in PQF model in updating and learning the important attributes for software quality assessment. The existing assessment techniques lack of the capability to rank the quality attributes and data learning which can enhance the quality assessment process. The aim of the study is to identify and propose the application of Artificial Intelligence (AI) technique for improving quality assessment technique in PQF model. Therefore, FRA using FRT was constructed and the performance of the FRA was evaluated. The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. The result shows that the performance of FRA correlates strongly to PQF model with 98% correlation compared to the Kolmogorov-Smirnov Correlation Based Filter (KSCBF) algorithm with 83% correlation. Statistical significance test was also performed with score of 0.052 compared to the KSCBF algorithm with score of 0.048. The result shows that the FRA was more significant than KSCBF algorithm. The main contribution of this research is on the implementation of FRT with proposed Most Priority of Features (MPF) calculation in FRA for attributes assessment. Overall, the findings and contributions can be regarded as a novel effort in software quality for attributes selection

An Empirical Study on the Procedure to Derive Software Quality Estimation Models

Software quality assurance has been a heated topic for several decades. If factors that influence software quality can be identified, they may provide more insight for better software development management. More precise quality assurance can be achieved by employing resources according to accurate quality estimation at the early stages of a project. In this paper, a general procedure is proposed to derive software quality estimation models and various techniques are presented to accomplish the tasks in respective steps. Several statistical techniques together with machine learning method are utilized to verify the effectiveness of software metrics. Moreover, a neuro-fuzzy approach is adopted to improve the accuracy of the estimation model. This procedure is carried out based on data from the ISBSG repository to present its empirical value

IMPLEMENTASI MODEL PEMBELAJARAN MESIN DENGAN METODE ENSAMBEL DAN TEKNIK SELEKSI FITUR PADA PREDIKSI TINGKAT KEMAMPUAN PEMELIHARAAN PERANGKAT LUNAK

Tingkat kemampuan pemeliharaan perangkat lunak merupakan salah satu atribut eksternal dasar dari kualitas perangkat lunak yang mengukur tingkat efektivitas dan efisiensi di mana suatu perangkat lunak dapat dimodifikasi oleh pemelihara perangkat lunak tersebut. Tingkat kemampuan pemeliharaan perangkat lunak diukur menggunakan prediksi sebuah model pembelajaran mesin berdasarkan sejumlah atribut kualitas perangkat lunak untuk mendukung dan membantu dalam pengambilan keputusan pada saat proses pemeliharaan perangkat lunak dilakukan. Sumber himpunan data baru yang terdiri dari lima dataset perangkat lunak berorientasi objek Java dengan tujuh belas jenis metrik tingkat kelas digunakan dalam penelitian ini. Model pembelajaran mesin dibangun dengan menggunakan beberapa model individu seperti Lasso Regression, K-Nearest Neighbors, Regression Tree, Multilayer Perceptron, M5Rules, Support Vector Machine, Artificial Neural Network, dan dengan menggunakan metode ensambel seperti Bagging dan AdaBoost. Selain itu, teknik seleksi fitur dipertimbangkan untuk mengidentifikasi fitur terbaik sehingga meningkatkan performa dari model prediksi. Penelitian ini bertujuan untuk menyelidiki performa berbagai sumber himpunan data dalam model pembelajaran mesin. Performa model ini di evaluasi dengan menggunakan tiga metrik evaluasi, yaitu MMRE, MAE, dan Pred. Hasil menunjukkan bahwa ANN menjadi algoritma terbaik pada model individu dengan MMRE 0.88 pada dataset Equinox Framework. Metode ensambel terbukti meningkatkan performa dari model dengan ketentuan metode ensambel cocok dengan algoritma individu yang digunakan. Performa terbaik didapatkan metode AdaBoost dengan ANN pada dataset Lucene dengan MMRE 0.78. Teknik seleksi fitur juga terbukti meningkatkan beberapa model prediksi dengan penghapusan fitur yang tepat dan algoritma yang digunakan cocok dengan distribusi datanya. ----- Software maintainability is one of the primary external attributes of software quality that measures the effectiveness and efficiency with which the software maintainer can modify the software. Software maintainability is measured using the prediction of machine learning models based on several software quality attributes to support and assist in decision-making during the software maintenance process. This study used new datasets consisting of five Java object-oriented software systems with seventeen class-level metrics. Machine learning models are built using several individual models such as Lasso Regression, K-Nearest Neighbors, Regression Tree, Multilayer Perceptron, M5Rules, Support Vector Machine, Artificial Neural Network, and by using ensemble methods such as Bagging and AdaBoost. In addition, feature selection techniques are considered to identify the best features, thereby increasing the prediction model's performance. This research aims to investigate the performance of various dataset sources in machine learning models. The performance of these models is evaluated using three evaluation metrics, namely MMRE, MAE, and Pred. The results show that ANN is the best algorithm for individual models with MMRE 0.88 on the Equinox Framework dataset. The ensemble method is proven to improve the performance of the model, provided that the ensemble method matches the individual algorithms used. The AdaBoost method obtained the best performance with ANN on the Lucene dataset with MMRE 0.78. The feature selection technique is also proven to improve several prediction models with the proper feature removal, and the algorithm matches the data distribution

Application of ensemble techniques in predicting object-oriented software maintainability

While prior object-oriented software maintainability literature acknowledges the role of machine learning techniques as valuable predictors of potential change, the most suitable technique that achieves consistently high accuracy remains undetermined. With the objective of obtaining more consistent results, an ensemble technique is investigated to advance the performance of the individual models and increase their accuracy in predicting software maintainability of the object-oriented system. This paper describes the research plan for predicting object-oriented software maintainability using ensemble techniques. First, we present a brief overview of the main research background and its different components. Second, we explain the research methodology. Third, we provide expected results. Finally, we conclude summary of the current status