The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Barry Boehm - One of the best experts on this subject based on the ideXlab platform.
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Maintenance Effort estimation for open source software a systematic literature review
International Conference on Software Maintenance, 2016Co-Authors: Lin Shi, Celia Chen, Qing Wang, Barry BoehmAbstract:Open Source Software (OSS) is distributed and maintained collaboratively by developers all over the world. However, frequent personnel turnover and lack of organizational management makes it difficult to capture the actual development Effort. Various OSS Maintenance Effort estimation approaches have been developed to provide a way to understand and estimate development Effort. The goal of this study is to identify the current state of art of the existing Maintenance Effort estimation approaches for OSS. We performed a systematic literature review on the relevant studies published in the period between 2000-2015 by both automatic and manual searches from different sources. We derived a set of keywords from the research questions and established selection criteria to carefully choose the papers to evaluate. 29 out of 3,312 papers were selected based on a well designed selection process. Our results show that the commonly used OSS Maintenance Effort estimation methods are actual Effort estimation and Maintenance activity time prediction, the most commonly used metrics and factors for actual Effort estimation are source code measurements and people related metrics, the most commonly mentioned activity for Maintenance activity time prediction is bug fixing. Accuracy measures and cross validation is used for validating the estimation models. Based on the above findings, we identified the issues in evaluation methods for actual Maintenance Effort estimations and the needs for quantitative OSS Maintenance Effort inference from size-related metrics. Meanwhile, we highlighted individual contribution and performance measurement as a novel and promising research area.
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a controlled experiment in assessing and estimating software Maintenance tasks
Information & Software Technology, 2011Co-Authors: Vu Nguyen, Barry Boehm, Phongphan DanphitsanuphanAbstract:Context: Software Maintenance is an important software engineering activity that has been reported to account for the majority of the software total cost. Thus, understanding the factors that influence the cost of software Maintenance tasks helps maintainers to make informed decisions about their work. Objective: This paper describes a controlled experiment of student programmers performing Maintenance tasks on a C++ program. The objective of the study is to assess the Maintenance size, Effort, and Effort distributions of three different Maintenance types and to describe estimation models to predict the programmer's Effort spent on Maintenance tasks. Method: Twenty-three graduate students and a senior majoring in computer science participated in the experiment. Each student was asked to perform Maintenance tasks required for one of the three task groups. The impact of different LOC metrics on Maintenance Effort was also evaluated by fitting the data collected into four estimation models. Results: The results indicate that corrective Maintenance is much less productive than enhancive and reductive Maintenance and program comprehension activities require as much as 50% of the total Effort in corrective Maintenance. Moreover, the best software Effort model can estimate the time of 79% of the programmers with the error of or less than 30%. Conclusion: Our study suggests that the LOC added, modified, and deleted metrics are good predictors for estimating the cost of software Maintenance. Effort estimation models for Maintenance work may use the LOC added, modified, deleted metrics as the independent parameters instead of the simple sum of the three. Another implication is that reducing business rules of the software requires a sizable proportion of the software Maintenance Effort. Finally, the differences in Effort distribution among the Maintenance types suggest that assigning Maintenance tasks properly is important to effectively and efficiently utilize human resources.
Qing Wang - One of the best experts on this subject based on the ideXlab platform.
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Maintenance Effort estimation for open source software a systematic literature review
International Conference on Software Maintenance, 2016Co-Authors: Lin Shi, Celia Chen, Qing Wang, Barry BoehmAbstract:Open Source Software (OSS) is distributed and maintained collaboratively by developers all over the world. However, frequent personnel turnover and lack of organizational management makes it difficult to capture the actual development Effort. Various OSS Maintenance Effort estimation approaches have been developed to provide a way to understand and estimate development Effort. The goal of this study is to identify the current state of art of the existing Maintenance Effort estimation approaches for OSS. We performed a systematic literature review on the relevant studies published in the period between 2000-2015 by both automatic and manual searches from different sources. We derived a set of keywords from the research questions and established selection criteria to carefully choose the papers to evaluate. 29 out of 3,312 papers were selected based on a well designed selection process. Our results show that the commonly used OSS Maintenance Effort estimation methods are actual Effort estimation and Maintenance activity time prediction, the most commonly used metrics and factors for actual Effort estimation are source code measurements and people related metrics, the most commonly mentioned activity for Maintenance activity time prediction is bug fixing. Accuracy measures and cross validation is used for validating the estimation models. Based on the above findings, we identified the issues in evaluation methods for actual Maintenance Effort estimations and the needs for quantitative OSS Maintenance Effort inference from size-related metrics. Meanwhile, we highlighted individual contribution and performance measurement as a novel and promising research area.
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estimating software Maintenance Effort from use cases an industrial case study
International Conference on Software Maintenance, 2011Co-Authors: Ye Yang, Qing WangAbstract:Software Maintenance Effort constitutes a major portion of the software lifecycle Effort. Its estimation is vital for successful project planning and strategic resource allocation. In this paper, we conduct and report an industrial case study in this field. The data set was collected from an industrial software process management tool QONE (formerly SoftPM). The methodology proposed provides corresponding guidance for Effort estimation in software evolutionary projects that employ use-cases in capturing Maintenance requirements. And the model, constructed using the linear regression analysis and validated by the leave-one-out cross-validation, provides an Effort prediction for the future Maintenance of the project. The analysis results indicate that the methodology can be applied at an early stage of the project life cycle and provides a good tradeoff among simplicity, early-estimating and accuracy in one estimate.
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an empirical analysis on distribution patterns of software Maintenance Effort
International Conference on Software Maintenance, 2008Co-Authors: Ye Yang, Qing WangAbstract:Distribution of Effort in software engineering process has been the basis for facilitating more reasonable software project planning. This paper reports empirical results on activity Effort distribution patterns of a series of industrial software Maintenance projects. The results show that with respect to different influencing factors, the projects demonstrate large variations in their activity Effort distribution, which necessitates appropriate adjustments to strategic planning.
Phongphan Danphitsanuphan - One of the best experts on this subject based on the ideXlab platform.
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a controlled experiment in assessing and estimating software Maintenance tasks
Information & Software Technology, 2011Co-Authors: Vu Nguyen, Barry Boehm, Phongphan DanphitsanuphanAbstract:Context: Software Maintenance is an important software engineering activity that has been reported to account for the majority of the software total cost. Thus, understanding the factors that influence the cost of software Maintenance tasks helps maintainers to make informed decisions about their work. Objective: This paper describes a controlled experiment of student programmers performing Maintenance tasks on a C++ program. The objective of the study is to assess the Maintenance size, Effort, and Effort distributions of three different Maintenance types and to describe estimation models to predict the programmer's Effort spent on Maintenance tasks. Method: Twenty-three graduate students and a senior majoring in computer science participated in the experiment. Each student was asked to perform Maintenance tasks required for one of the three task groups. The impact of different LOC metrics on Maintenance Effort was also evaluated by fitting the data collected into four estimation models. Results: The results indicate that corrective Maintenance is much less productive than enhancive and reductive Maintenance and program comprehension activities require as much as 50% of the total Effort in corrective Maintenance. Moreover, the best software Effort model can estimate the time of 79% of the programmers with the error of or less than 30%. Conclusion: Our study suggests that the LOC added, modified, and deleted metrics are good predictors for estimating the cost of software Maintenance. Effort estimation models for Maintenance work may use the LOC added, modified, deleted metrics as the independent parameters instead of the simple sum of the three. Another implication is that reducing business rules of the software requires a sizable proportion of the software Maintenance Effort. Finally, the differences in Effort distribution among the Maintenance types suggest that assigning Maintenance tasks properly is important to effectively and efficiently utilize human resources.
Irfan Ahmad - One of the best experts on this subject based on the ideXlab platform.
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three empirical studies on predicting software maintainability using ensemble methods
Soft Computing, 2015Co-Authors: Mahmoud O Elish, Hamoud Aljamaan, Irfan AhmadAbstract:More accurate prediction of software Maintenance Effort contributes to better management and control of software Maintenance. Several research studies have recently investigated the use of computational intelligence models for software maintainability prediction. The performance of these models, however, may vary from dataset to dataset. Consequently, ensemble methods have become increasingly popular as they take advantage of the capabilities of their constituent computational intelligence models toward a dataset to come up with more accurate or at least competitive prediction accuracy compared to individual models. This paper investigates and empirically evaluates different homogenous and heterogeneous ensemble methods in predicting software Maintenance Effort and change proneness. Three major empirical studies were designed and conducted taken into consideration different design such as the types of the investigated ensembles methods, types of prediction problems, used datasets, and other experimental setup. Overall empirical evidence obtained from the three studies confirms that some ensemble methods provide more accurate or at least competitive prediction accuracy compared to individual models across datasets, and thus they are more reliable.
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an ensemble of computational intelligence models for software Maintenance Effort prediction
International Conference on Artificial Neural Networks, 2013Co-Authors: Hamoud Aljamaan, Mahmoud O Elish, Irfan AhmadAbstract:More accurate prediction of software Maintenance Effort contributes to better management and control of software Maintenance. Several research studies have recently investigated the use of computational intelligence models for software maintainability prediction. The performance of these models however may vary from dataset to dataset. Consequently, computational intelligence ensemble techniques have become increasingly popular as they take advantage of the capabilities of their constituent models toward a dataset to come up with more accurate or at least competitive prediction accuracy compared to individual models. This paper proposes and empirically evaluates an ensemble of computational intelligence models for predicting software Maintenance Effort. The results confirm that the proposed ensemble technique provides more accurate prediction compared to individual models, and thus it is more reliable.
A K Misra - One of the best experts on this subject based on the ideXlab platform.
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dynamic software Maintenance Effort estimation modeling using neural network rule engine and multi regression approach
International Conference on Computational Science and Its Applications, 2012Co-Authors: Ruchi Shukla, A K Misra, Mukul Shukla, Tshilidzi Marwala, W A ClarkeAbstract:The dynamic business environment of software projects typically involves a large number of technical, demographic and environmental variables. This coupled with imprecise data on human, management and dynamic factors makes the objective estimation of software development and Maintenance Effort a very challenging task. Currently, no single estimation model or tool has been able to coherently integrate and realistically address the above problems. This paper presents a multi-fold modeling approach using neural network, rule engine and multi-regression for dynamic software Maintenance Effort estimation. The system dynamics modeling tool developed using quantitative and qualitative inputs from real life projects is able to successfully simulate and validate the dynamic behavior of a software Maintenance estimation system.
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software Maintenance Effort estimation neural network vs regression modeling approach
International Journal of Computer Applications, 2010Co-Authors: Ruchi Shukla, A K MisraAbstract:The global IT industry has now matured. As more and more systems grow old and enter into the Maintenance stage, software Maintenance (SM) is becoming one of the most carried out and challenging tasks. Besides, the industry is also facing a shift in traditional technical environment by way of use of newer tools and approaches of software development, migration from legacy software to current software and dynamic changes in the SM environment. The challenge then lies in accurately modeling and predicting the SM Effort, schedule and risk involved, under the above circumstances. This work employs a neural network (NN) approach to model and predict the software Maintenance Effort based on an available real life dataset of outsourced Maintenance projects (Rao and Sarda, 36 projects of 14 drivers). A comparison between results obtained by NN and regression modeling is also presented. It is concluded that NN is able to successfully model the complex, non-linear relationship between a large number of Effort drivers and the software Maintenance Effort, with results closely matching the Effort estimated by experts.
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ai based framework for dynamic modeling of software Maintenance Effort estimation
International Conference on Computer and Automation Engineering, 2009Co-Authors: Ruchi Shukla, A K MisraAbstract:With the growth of IT and software industry there has been a shift in the software development paradigm from traditional techniques based to object-oriented and component-based development techniques. Due to this shift dynamic changes occur in the technology, environment and many other qualitative/quantitative factors, leading to increased Maintenance Effort. The challenge then lies inaccurately modeling and estimating the software Maintenance Effort under such dynamically emerging circumstances. This paper summarizes some of the recent advances in the field of dynamic software Maintenance Effort estimation. Various new static and dynamic factors and their roles in Effort prediction are discussed, and based on them a dynamic modeling framework is proposed. Artificial intelligence is contemplated in this research, mainly functional networks based. Further, the proposed framework needs to be validated with real life project data.
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estimating software Maintenance Effort a neural network approach
India Software Engineering Conference, 2008Co-Authors: Ruchi Shukla, A K MisraAbstract:Software Maintenance forms an essential component of software development. Its planning includes estimation of Maintenance Effort, duration, personnel and costs. Adequate information regarding size, complexity and maintainability is however often unavailable. In the present work, a Neural Network (NN) based Effort estimator is developed using Matlab. A feed forward back- propagation NN employing Bayesian regularization training is selected and trained for one dataset. Various categories of software Maintenance cost drivers and their effect on Maintenance Effort have been analyzed using different combinations of number of hidden layers and hidden neurons etc. The NN is able to successfully model the Maintenance Effort as the obtained results are well within the previously published error limits
