Network-Based Analysis of Software Change Propagation

Wang, Rongcun; Huang, Rubing; Qu, Binbin

doi:10.1155/2014/237243

Cited by 10 publications

(7 citation statements)

References 32 publications

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“…Engineer CPI is calculated based on the number of incoming and outgoing workflows of the engineer in Engineer DSM, and PD is the geodesic shortest path from a component to another in Component DSM if there is a change propagated between those components in Change DSM (Pasqual and Weck 2011). Wang et al utilized network centrality metrics and their derivatives, which reflect the importance of nodes in the network in spreading information to identify their correlation with the scope of change propagation in a software class structure network (Wang et al 2014). Colombo et al investigated the architectural network features such as the number of components, modules, components in modules, bus elements, etc., on change propagation behavior of complex technical systems (Colombo et al 2015).…”

Section: Network Approachesmentioning

confidence: 99%

Employing machine learning techniques to assess requirement change volatility

et al. 2021

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Lack of planning when changing requirements to reflect stakeholders’ expectations can lead to propagated changes that can cause project failures. Existing tools cannot provide the formal reasoning required to manage requirement change and minimize unanticipated change propagation. This research explores machine learning techniques to predict requirement change volatility (RCV) using complex network metrics based on the premise that requirement networks can be utilized to study change propagation. Three research questions (RQs) are addressed: (1) Can RCV be measured through four classes namely, multiplier, absorber, transmitter, and robust, during every instance of change? (2) Can complex network metrics be explored and computed for each requirement during every instance of change? (3) Can machine learning techniques, specifically, multilabel learning (MLL) methods be employed to predict RCV using complex network metrics? RCV in this paper quantifies volatility for change propagation, that is, how requirements behave in response to the initial change. A multiplier is a requirement that is changed by an initial change and propagates change to other requirements. An absorber is a requirement that is changed by an initial change, but does not propagate change to other requirements. A transmitter is a requirement that is not changed by an initial change, but propagates change to other requirements. A robust requirement is a requirement that is not changed by an initial change and does not propagate change to other requirements. RCV is determined using industrial data and requirement network relationships obtained from previously developed Refined Automated Requirement Change Propagation Prediction (R-ARCPP) tool. Useful complex network metrics in highest performing machine learning models are discussed along with the limitations and future directions of this research.

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Section: Network Approachesmentioning

confidence: 99%

Employing machine learning techniques to assess requirement change volatility

et al. 2021

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“…Loch and Terwiesch [12] suggested that the reliability of upstream activities should be measured by the number of errors that occurred in the specific period of upstream activities. Some other scholars used the DSM matrix to study the dependence between elements in the system [16][17][18].…”

Section: Literature Reviewmentioning

confidence: 99%

“…e research aiming at solving such problems mainly focuses on the degree of dependence between activities; that is, the impact of changes in one activity on another activity. Information sensitivity modeling [2,[11][12][13][14][15] and DSM matrix [16][17][18] are the two key methods for this category. However, most of them neglected that the activity maturity is various according to time [13,15,19], which is an important prerequisite for subsequent analysis.…”

Section: Introductionmentioning

confidence: 99%

Model and Algorithm for Dependent Activity Schedule Optimization Combining with BIM

Guo

Liu

2020

Advances in Civil Engineering

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The project duration can be shortened by overlapping construction activities. However, the continuous changing of the environment tends to cause problems such as rework and the failure of the overlapping plan. In order to solve these problems, communication strategies for the overlapping of dependent activities are first introduced and optimized from a revenue perspective. We first consider the different maturities of upstream activity before and after the overlapping, the downstream sensitivity which is decided by involving communication strategies, and the learning and error-correcting ability of workers. Then, the overlap and communication strategies are decided by calculating the maximum revenue using Monte Carlo simulation and MATLAB based on overlap cost, communication cost, rework cost, and reward amount. Finally, the algorithm and BIM are combined to provide a visual overlap plan and dynamic control platform framework. This research is valuable for practitioners as it provides a dynamic overlap plan which can maximize the revenue in changing the environment and ensure the duration of the project. This research also provides researchers a new insight into combining overlap problems and BIM technology.

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“…In [59], the author developed a new centrality metric called CIRank (that keeps track of the changes propagating among classes in a software dependency network) and observed it to be significantly correlated with the degree and PageRank centrality metrics on the basis of the Spearman's rank-based correlation coefficient. In [60], the Kendall's concordance-based correlation measure was used to assess the correlation between eight different centrality metrics that are suitable for gene regulatory networks in E. Coli.…”

Section: Related Work and Our Contributionsmentioning

confidence: 99%

Concordance-based Kendall's Correlation for Computationally-Light vs. Computationally-Heavy Centrality Metrics: Lower Bound for Correlation

Meghanathan

2017

CIT

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We identify three different levels of correlation (pairwise relative ordering, network-wide ranking and prediction through linearity) that could be assessed between a computationally-light centrality metric and a computationally-heavy centrality metric for real-world networks. The Kendall's concordance-based correlation measure could be used to quantitatively assess how well we could consider the relative ordering of two vertices vi and vj with respect to a computationally-light centrality metric as the relative ordering of the same two vertices with respect to a computationally-heavy centrality metric. We hypothesize that the pair-wise relative ordering (concordance)-based assessment of the correlation between centrality metrics is the strictest of all three levels of correlation and claim that the Kendall's concordance-based correlation coefficient will be lower than the correlation coefficient observed with the more relaxed levels of correlation measures (prediction through linearity-based Pearson's product-moment correlation coefficient and the network wide ranking-based Spearman's correlation coefficient). We validate our hypothesis by evaluating the three correlation coefficients between two sets of centrality metrics: the computationally-light degree and local clustering coefficient complement-based degree centrality metrics and the computationally-heavy eigenvector centrality, betweenness centrality and closeness centrality metrics for a diverse collection of 50 real-world networks.

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Network-Based Analysis of Software Change Propagation

Cited by 10 publications

References 32 publications

Employing machine learning techniques to assess requirement change volatility

Employing machine learning techniques to assess requirement change volatility

Model and Algorithm for Dependent Activity Schedule Optimization Combining with BIM

Concordance-based Kendall's Correlation for Computationally-Light vs. Computationally-Heavy Centrality Metrics: Lower Bound for Correlation

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