The Experts below are selected from a list of 174 Experts worldwide ranked by ideXlab platform

Abha Jain - One of the best experts on this subject based on the ideXlab platform.

  • predicting software maintenance effort by mining software project reports using inter version validation
    International Journal of Reliability Quality and Safety Engineering, 2016
    Co-Authors: Rajni Jindal, Ruchika Malhotra, Abha Jain
    Abstract:

    Changes in the software are unavoidable due to an ever changing dynamic and active environment wherein expectations and requirements of the users tend to change rapidly. As a result, software needs to upgrade itself from its previous version to the next version in order to meet expectations of the user. The upgradation of the software is in terms of total number of Lines of Code (LOC) that might have been inserted, deleted or modified in moving from one version of software to the next. These changes are maintained in the change reports which constitute of the defect ID and defect description. Defect description describes the cause of defect which might have occurred in the previous version of the software due to which either new LOC needs to be inserted or existing LOC need to be deleted or modified. A lot of effort is required to correct the defects identified in software at the maintenance phase i.e., when software is delivered at the customers end. Thus, in this paper, we intend to predict maintenance effort by analyzing the defect reports using text mining techniques and thereafter developing the prediction models using suitable machine learning algorithms viz. Multi-Layer Perceptron (MLP), Radial-Basis Function (RBF) network and Decision Tree (DT). We have considered the changes between three successive versions of ‘MMS’ Application Package of Android operating system and have performed inter-version validation where the model predicted using the version ‘v’ is validated on the subsequent version i.e., ‘v+1’. The performance of the model was evaluated using Receiver Operating Characteristics (ROC) analysis. The results indicated that the model predicted on ‘MMS’ 4.0 version using MLP algorithm has shown good results when validated on ‘MMS’ 4.1 version. On the other hand, the performance of RBF and DT algorithms has been consistently average in predicting the maintenance effort.

  • mining defect reports for predicting software maintenance effort
    Advances in Computing and Communications, 2015
    Co-Authors: Rajni Jindal, Ruchika Malhotra, Abha Jain
    Abstract:

    Software Maintenance is the crucial phase of software development lifecycle, which begins once the software has been deployed at the customer's site. It is a very broad activity and includes almost everything that is done to change the software if required, to keep it operational after its delivery at the customer's end. A lot of maintenance effort is required to change the software after it is in operation. Therefore, predicting the effort and cost associated with the maintenance activities such as correcting and fixing the defects has become one of the key issues that need to be analyzed for effective resource allocation and decision-making. In view of this issue, we have developed a model based on text mining techniques using the statistical method namely, Multi-nominal Multivariate Logistic Regression (MMLR). We apply text mining techniques to identify the relevant attributes from defect reports and relate these relevant attributes to software maintenance effort prediction. The proposed model is validated using ‘Camera’ Application Package of Android Operating System. Receiver Operating Characteristics (ROC) analysis is done to interpret the results obtained from model prediction by using the value of Area Under the Curve (AUC), sensitivity and a suitable threshold criterion known as the cut-off point. It is evident from the results that the performance of the model is dependent on the number of words considered for classification and therefore shows the best results with respect to top-100 words. The performance is irrespective of the type of effort category.

  • predicting software maintenance effort using neural networks
    International Conference on Computer Communications, 2015
    Co-Authors: Rajni Jindal, Ruchika Malhotra, Abha Jain
    Abstract:

    Software Maintenance is an important phase of software development lifecycle, which starts once the software has been deployed at the customer's end. A lot of maintenance effort is required to change the software after it is in operation. Therefore, predicting the effort and cost associated with the maintenance activities such as correcting and fixing the defects has become one of the key issues that need to be analyzed for effective resource allocation and decision-making. In view of this issue, we have developed a model based on text mining techniques using machine learning method namely, Radial Basis Function of neural network. We apply text mining techniques to identify the relevant attributes from defect reports and relate these relevant attributes to software maintenance effort prediction. The proposed model is validated using ‘Browser’ Application Package of Android Operating System. Receiver Operating Characteristics (ROC) analysis is done to interpret the results obtained from model prediction by using the value of Area Under the Curve (AUC), sensitivity and a suitable threshold criterion known as the cut-off point. It is evident from the results that the performance of the model is dependent on the number of words considered for classification and therefore shows the best results with respect to top-100 words. The performance is irrespective of the type of effort category.

Rajni Jindal - One of the best experts on this subject based on the ideXlab platform.

  • predicting software maintenance effort by mining software project reports using inter version validation
    International Journal of Reliability Quality and Safety Engineering, 2016
    Co-Authors: Rajni Jindal, Ruchika Malhotra, Abha Jain
    Abstract:

    Changes in the software are unavoidable due to an ever changing dynamic and active environment wherein expectations and requirements of the users tend to change rapidly. As a result, software needs to upgrade itself from its previous version to the next version in order to meet expectations of the user. The upgradation of the software is in terms of total number of Lines of Code (LOC) that might have been inserted, deleted or modified in moving from one version of software to the next. These changes are maintained in the change reports which constitute of the defect ID and defect description. Defect description describes the cause of defect which might have occurred in the previous version of the software due to which either new LOC needs to be inserted or existing LOC need to be deleted or modified. A lot of effort is required to correct the defects identified in software at the maintenance phase i.e., when software is delivered at the customers end. Thus, in this paper, we intend to predict maintenance effort by analyzing the defect reports using text mining techniques and thereafter developing the prediction models using suitable machine learning algorithms viz. Multi-Layer Perceptron (MLP), Radial-Basis Function (RBF) network and Decision Tree (DT). We have considered the changes between three successive versions of ‘MMS’ Application Package of Android operating system and have performed inter-version validation where the model predicted using the version ‘v’ is validated on the subsequent version i.e., ‘v+1’. The performance of the model was evaluated using Receiver Operating Characteristics (ROC) analysis. The results indicated that the model predicted on ‘MMS’ 4.0 version using MLP algorithm has shown good results when validated on ‘MMS’ 4.1 version. On the other hand, the performance of RBF and DT algorithms has been consistently average in predicting the maintenance effort.

  • mining defect reports for predicting software maintenance effort
    Advances in Computing and Communications, 2015
    Co-Authors: Rajni Jindal, Ruchika Malhotra, Abha Jain
    Abstract:

    Software Maintenance is the crucial phase of software development lifecycle, which begins once the software has been deployed at the customer's site. It is a very broad activity and includes almost everything that is done to change the software if required, to keep it operational after its delivery at the customer's end. A lot of maintenance effort is required to change the software after it is in operation. Therefore, predicting the effort and cost associated with the maintenance activities such as correcting and fixing the defects has become one of the key issues that need to be analyzed for effective resource allocation and decision-making. In view of this issue, we have developed a model based on text mining techniques using the statistical method namely, Multi-nominal Multivariate Logistic Regression (MMLR). We apply text mining techniques to identify the relevant attributes from defect reports and relate these relevant attributes to software maintenance effort prediction. The proposed model is validated using ‘Camera’ Application Package of Android Operating System. Receiver Operating Characteristics (ROC) analysis is done to interpret the results obtained from model prediction by using the value of Area Under the Curve (AUC), sensitivity and a suitable threshold criterion known as the cut-off point. It is evident from the results that the performance of the model is dependent on the number of words considered for classification and therefore shows the best results with respect to top-100 words. The performance is irrespective of the type of effort category.

  • predicting software maintenance effort using neural networks
    International Conference on Computer Communications, 2015
    Co-Authors: Rajni Jindal, Ruchika Malhotra, Abha Jain
    Abstract:

    Software Maintenance is an important phase of software development lifecycle, which starts once the software has been deployed at the customer's end. A lot of maintenance effort is required to change the software after it is in operation. Therefore, predicting the effort and cost associated with the maintenance activities such as correcting and fixing the defects has become one of the key issues that need to be analyzed for effective resource allocation and decision-making. In view of this issue, we have developed a model based on text mining techniques using machine learning method namely, Radial Basis Function of neural network. We apply text mining techniques to identify the relevant attributes from defect reports and relate these relevant attributes to software maintenance effort prediction. The proposed model is validated using ‘Browser’ Application Package of Android Operating System. Receiver Operating Characteristics (ROC) analysis is done to interpret the results obtained from model prediction by using the value of Area Under the Curve (AUC), sensitivity and a suitable threshold criterion known as the cut-off point. It is evident from the results that the performance of the model is dependent on the number of words considered for classification and therefore shows the best results with respect to top-100 words. The performance is irrespective of the type of effort category.

Ruchika Malhotra - One of the best experts on this subject based on the ideXlab platform.

  • predicting software maintenance effort by mining software project reports using inter version validation
    International Journal of Reliability Quality and Safety Engineering, 2016
    Co-Authors: Rajni Jindal, Ruchika Malhotra, Abha Jain
    Abstract:

    Changes in the software are unavoidable due to an ever changing dynamic and active environment wherein expectations and requirements of the users tend to change rapidly. As a result, software needs to upgrade itself from its previous version to the next version in order to meet expectations of the user. The upgradation of the software is in terms of total number of Lines of Code (LOC) that might have been inserted, deleted or modified in moving from one version of software to the next. These changes are maintained in the change reports which constitute of the defect ID and defect description. Defect description describes the cause of defect which might have occurred in the previous version of the software due to which either new LOC needs to be inserted or existing LOC need to be deleted or modified. A lot of effort is required to correct the defects identified in software at the maintenance phase i.e., when software is delivered at the customers end. Thus, in this paper, we intend to predict maintenance effort by analyzing the defect reports using text mining techniques and thereafter developing the prediction models using suitable machine learning algorithms viz. Multi-Layer Perceptron (MLP), Radial-Basis Function (RBF) network and Decision Tree (DT). We have considered the changes between three successive versions of ‘MMS’ Application Package of Android operating system and have performed inter-version validation where the model predicted using the version ‘v’ is validated on the subsequent version i.e., ‘v+1’. The performance of the model was evaluated using Receiver Operating Characteristics (ROC) analysis. The results indicated that the model predicted on ‘MMS’ 4.0 version using MLP algorithm has shown good results when validated on ‘MMS’ 4.1 version. On the other hand, the performance of RBF and DT algorithms has been consistently average in predicting the maintenance effort.

  • mining defect reports for predicting software maintenance effort
    Advances in Computing and Communications, 2015
    Co-Authors: Rajni Jindal, Ruchika Malhotra, Abha Jain
    Abstract:

    Software Maintenance is the crucial phase of software development lifecycle, which begins once the software has been deployed at the customer's site. It is a very broad activity and includes almost everything that is done to change the software if required, to keep it operational after its delivery at the customer's end. A lot of maintenance effort is required to change the software after it is in operation. Therefore, predicting the effort and cost associated with the maintenance activities such as correcting and fixing the defects has become one of the key issues that need to be analyzed for effective resource allocation and decision-making. In view of this issue, we have developed a model based on text mining techniques using the statistical method namely, Multi-nominal Multivariate Logistic Regression (MMLR). We apply text mining techniques to identify the relevant attributes from defect reports and relate these relevant attributes to software maintenance effort prediction. The proposed model is validated using ‘Camera’ Application Package of Android Operating System. Receiver Operating Characteristics (ROC) analysis is done to interpret the results obtained from model prediction by using the value of Area Under the Curve (AUC), sensitivity and a suitable threshold criterion known as the cut-off point. It is evident from the results that the performance of the model is dependent on the number of words considered for classification and therefore shows the best results with respect to top-100 words. The performance is irrespective of the type of effort category.

  • predicting software maintenance effort using neural networks
    International Conference on Computer Communications, 2015
    Co-Authors: Rajni Jindal, Ruchika Malhotra, Abha Jain
    Abstract:

    Software Maintenance is an important phase of software development lifecycle, which starts once the software has been deployed at the customer's end. A lot of maintenance effort is required to change the software after it is in operation. Therefore, predicting the effort and cost associated with the maintenance activities such as correcting and fixing the defects has become one of the key issues that need to be analyzed for effective resource allocation and decision-making. In view of this issue, we have developed a model based on text mining techniques using machine learning method namely, Radial Basis Function of neural network. We apply text mining techniques to identify the relevant attributes from defect reports and relate these relevant attributes to software maintenance effort prediction. The proposed model is validated using ‘Browser’ Application Package of Android Operating System. Receiver Operating Characteristics (ROC) analysis is done to interpret the results obtained from model prediction by using the value of Area Under the Curve (AUC), sensitivity and a suitable threshold criterion known as the cut-off point. It is evident from the results that the performance of the model is dependent on the number of words considered for classification and therefore shows the best results with respect to top-100 words. The performance is irrespective of the type of effort category.

Irina Zotkina - One of the best experts on this subject based on the ideXlab platform.

  • about development and verification of software for finite element analysis of beam systems
    Procedia Engineering, 2015
    Co-Authors: Sergey Zotkin, Nina S Blokhina, Irina Zotkina
    Abstract:

    Abstract The distinctive paper is devoted to author's software (Windows Application) for finite element analysis (FEA) of beam systems. Program Application Package provides convenient graphical interface to the user for quick creation of layout design in accordance with given coordinates of nodes. User can specify various boundary conditions in the corresponding nodes or apply external loads. The results of FEA are displayed in text and graphical forms (including visualization of deformed scheme). Moreover, these results can be exported into a Microsoft Word document. Open source and freeware program has been created by Microsoft Visual C++. Information about verification samples is presentedClick

John Meier - One of the best experts on this subject based on the ideXlab platform.