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Kuei-chen Chiu - One of the best experts on this subject based on the ideXlab platform.
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A discussion of software reliability growth models with time-varying Learning Effects
American Journal of Software Engineering and Applications, 2020Co-Authors: Kuei-chen ChiuAbstract:Over the last few decades, software reliability growth models (SRGM) has been developed to predict software reliability in the testing/debugging phase. Most of the models are based on the Non-Homogeneous Poisson Process (NHPP), and an S or exponential-shaped type of testing behavior is usually assumed. Chiu et al. (2008) provided an SRGM that considers Learning Effects, which is able to reasonably describe the S and exponential-shaped behaviors simultaneously. This paper considers both linear and exponential-Learning Effects in an SRGM to enhance the model in Chiu et al. (2008), assumes the Learning Effects depend on the testing-time, and discusses when and what Learning Effects would occur in the software development process. This research also verifies the effectiveness of the proposed models with R square (Rsq), and compares the results with these of other models by using four real datasets. The proposed models consider constant, linear, and exponential-Learning Effects simultaneously. The results reveal the proposed models fit the data better than other models, and that the Learning Effects occur in the software testing process. The results are helpful for the software testing/debugging managers to master the schedule of the projects, the performance of the programmers, and the reliability of the software system.
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A discussion of multiple Learning Effects and unconscious behavior in the software debugging process with variable potential errors and change-points
2013 IEEE International Conference on Industrial Engineering and Engineering Management, 2013Co-Authors: Kuei-chen Chiu, Shulan HsiehAbstract:Cognitive Learning has been applied in various fields for the purpose of discussing human behavior. In this study, we employ Learning functions in software reliability growth models (SRGMs) and consider the conscious/unconscious behavior and multiple Learning Effects in the models simultaneously to discuss the influence of Learning Effects on work performance in software debugging projects and also the influence of variations in the environment on Learning Effects. The models are based on Chiu's models [2] and employed a sine function [15] to describe the variable potential errors and to judge change-points in the software debugging process. The results showed almost perfect fitting for the models to the actual data sets, which means the staff engaged in software debugging projects have not only conscious Learning Effects but also unconscious behavior that can describe variable potential errors and explain the change-points in a software debugging process. This paper also examines the effectiveness of the proposed models and discuss when and what kinds of Learning Effects occur and how these influence software reliability those help managers to master the software debugging process, the performance of the staff involved in this process, the reliability of the software system, and in the employment of suitable methods to manage organizations.
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An application of Learning Effects for assessing work performance using a software reliability growth model with multiple change-points
2013 IEEE International Conference on Industrial Engineering and Engineering Management, 2013Co-Authors: Kuei-chen Chiu, Shulan HsiehAbstract:Learning Effects exist with regard to various behaviors, and especially work-related processes. This study measures the performance of a software testing project with time-varying Effects using a software reliability growth model (SRGM), and discusses the changes in Learning Effects parameters with change-points in the model. We employ Chiu's [2] model to construct the time-varying Learning Effects and measure the performance of the software testing project using the data set in Huang and Hung [10]. This paper also discusses the time-lag between error-detected and error-removed that exists with different Learning concepts. The results indicate that error-removed requires more cognitive Learning process time, and this information can be used to help project managers mastering the staff and the process of software testing, efficiently.
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A study of software reliability growth model for time-dependent Learning Effects
2012 IEEE International Conference on Industrial Engineering and Engineering Management, 2012Co-Authors: Kuei-chen ChiuAbstract:This paper considered time-dependent Learning Effects in the software reliability growth model which Chiu et al. (2008) provided from the perspective of Learning Effects and would be able to reasonably describe the S-shaped and exponential-shaped types of behaviors simultaneously, and had better performance in fitting different data with consideration of a constant Learning effect to enhance the model. This study assumed Learning Effects were depend on the process time and improved the model with linear-Learning effect and exponential-Learning effect to discuss when and what Learning Effects would occur in the software development process. This paper also verified the effectiveness of the proposed model with R square (Rsq) and compared with other models by using the comparison criteria with real data set. The results revealed that the proposed model shows good fitting in the data set which software development process exists time-dependent Learning Effects.
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An improved model of software reliability growth under time-dependent Learning Effects
2011 IEEE International Conference on Quality and Reliability, 2011Co-Authors: Kuei-chen ChiuAbstract:Over the last two decades, various software reliability growth models (SRGM) have been proposed, and there has been a gradual but marked shift in the balance between software reliability and software testing cost in recent years. Chiu and Huang (2008) provided a Software Reliability Growth Model from the Perspective of Learning Effects, which is able to reasonably describe the S-shaped and exponential-shaped types of behaviors simultaneously, and offers better performance when fitting different data with consideration of the Learning Effects. However, this earlier model assumes that the Learning Effects are constant. In contrast, this paper discusses a software reliability growth model with time-dependent Learning Effects.
Cees P. M. Van Der Vleuten - One of the best experts on this subject based on the ideXlab platform.
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Modelling the pre-assessment Learning Effects of assessment : evidence in the validity chain
Medical Education, 2012Co-Authors: Francois Cilliers, Cees P. M. Van Der VleutenAbstract:OBJECTIVES: We previously developed a model of the pre-assessment Learning Effects of consequential assessment and started to validate it. The model comprises assessment factors, mechanism factors and Learning Effects. The purpose of this study was to continue the validation process. For stringency, we focused on a subset of assessment factor-Learning effect associations that featured least commonly in a baseline qualitative study. Our aims were to determine whether these uncommon associations were operational in a broader but similar population to that in which the model was initially derived. METHODS: A cross-sectional survey of 361 senior medical students at one medical school was undertaken using a purpose-made questionnaire based on a grounded theory and comprising pairs of written situational tests. In each pair, the manifestation of an assessment factor was varied. The frequencies at which Learning Effects were selected were compared for each item pair, using an adjusted alpha to assign significance. The frequencies at which mechanism factors were selected were calculated. RESULTS: There were significant differences in the Learning effect selected between the two scenarios of an item pair for 13 of this subset of 21 uncommon associations, even when a p-value of < 0.00625 was considered to indicate significance. Three mechanism factors were operational in most scenarios: agency; response efficacy, and response value. CONCLUSIONS: For a subset of uncommon associations in the model, the role of most assessment factor-Learning effect associations and the mechanism factors involved were supported in a broader but similar population to that in which the model was derived. Although model validation is an ongoing process, these results move the model one step closer to the stage of usefully informing interventions. Results illustrate how factors not typically included in studies of the Learning Effects of assessment could confound the results of interventions aimed at using assessment to influence Learning.
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A model of the pre-assessment Learning Effects of assessment is operational in an undergraduate clinical context
BMC Medical Education, 2012Co-Authors: Francois Cilliers, Lambert Schuwirth, Cees P. M. Van Der VleutenAbstract:Background No validated model exists to explain the Learning Effects of assessment, a problem when designing and researching assessment for Learning. We recently developed a model explaining the pre-assessment Learning Effects of summative assessment in a theory teaching context. The challenge now is to validate this model. The purpose of this study was to explore whether the model was operational in a clinical context as a first step in this process.
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A Model of the Pre-assessment Learning Effects of Summative Assessment in Medical Education.
Advances in Health Sciences Education, 2011Co-Authors: Francois Cilliers, Nicoline Herman, Hanelie Adendorff, Cees P. M. Van Der VleutenAbstract:It has become axiomatic that assessment impacts powerfully on student Learning. However, surprisingly little research has been published emanating from authentic higher education settings about the nature and mechanism of the pre-assessment Learning Effects of summative assessment. Less still emanates from health sciences education settings. This study explored the pre-assessment Learning Effects of summative assessment in theoretical modules by exploring the variables at play in a multifaceted assessment system and the relationships between them. Using a grounded theory strategy, in-depth interviews were conducted with individual medical students and analyzed qualitatively. Respondents’ Learning was influenced by task demands and system design. Assessment impacted on respondents’ cognitive processing activities and metacognitive regulation activities. Individually, our findings confirm findings from other studies in disparate non-medical settings and identify some new factors at play in this setting. Taken together, findings from this study provide, for the first time, some insight into how a whole assessment system influences student Learning over time in a medical education setting. The findings from this authentic and complex setting paint a nuanced picture of how intricate and multifaceted interactions between various factors in an assessment system interact to influence student Learning. A model linking the sources, mechanism and consequences of the pre-assessment Learning Effects of summative assessment is proposed that could help enhance the use of summative assessment as a tool to augment Learning.
Dar-li Yang - One of the best experts on this subject based on the ideXlab platform.
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Scheduling with deteriorating jobs and Learning Effects
Applied Mathematics and Computation, 2011Co-Authors: Dar-li YangAbstract:Abstract This paper studies a single machine scheduling problem simultaneously with deteriorating jobs and Learning Effects. The objectives are to minimize the makespan and the number of tardy jobs, respectively. Two polynomial time algorithms are proposed to solve these problems optimally.
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Some scheduling problems with deteriorating jobs and Learning Effects
Computers & Industrial Engineering, 2010Co-Authors: Dar-li YangAbstract:This paper considers some scheduling problems with deteriorating jobs and Learning Effects. The following objective functions are considered: the makespan, the total completion times, and the total absolute differences in completion times. Several polynomial time algorithms are proposed to optimally solve the single-machine scheduling problems. Finally, we show that several special cases of the flowshop scheduling problems remain polynomially solvable under the proposed model.
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A note on due-date assignment and single-machine scheduling with deteriorating jobs and Learning Effects
Journal of the Operational Research Society, 2010Co-Authors: Dar-li YangAbstract:The concepts of deteriorating jobs and Learning Effects have been individually studied in many scheduling problems. This note considers a single-machine scheduling problem with deteriorating jobs and Learning Effects. All of the jobs have a common (but unknown) due date. The objective is to minimize the sum of the weighted earliness, tardiness and due-date penalties. An O(n3) algorithm is proposed to optimally solve the problem with deteriorating jobs and job-dependent Learning effect. Besides, an O(n log n) algorithm is provided to solve the problem with deteriorating jobs and job-independent Learning effect.
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Single-machine scheduling with both deterioration and Learning Effects
Annals of Operations Research, 2009Co-Authors: Dar-li YangAbstract:This paper considers a single-machine scheduling problem with both deterioration and Learning Effects. The objectives are to respectively minimize the makespan, the total completion times, the sum of weighted completion times, the sum of the kth power of the job completion times, the maximum lateness, the total absolute differences in completion times and the sum of earliness, tardiness and common due-date penalties. Several polynomial time algorithms are proposed to optimally solve the problem with the above objectives.
Ji-bo Wang - One of the best experts on this subject based on the ideXlab platform.
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Several single-machine scheduling problems with general Learning Effects
Applied Mathematical Modelling, 2012Co-Authors: Yuan-yuan Lu, Ji-bo WangAbstract:Abstract In this paper we consider several single-machine scheduling problems with general Learning Effects. By general Learning Effects, we mean that the processing time of a job depends not only on its scheduled position, but also on the total normal processing time of the jobs already processed. We show that the scheduling problems of minimization of the makespan, the total completion time and the sum of the θth ( θ ⩾ 0 ) power of job completion times can be solved in polynomial time under the proposed models. We also prove that some special cases of the total weighted completion time minimization problem and the maximum lateness minimization problem can be solved in polynomial time.
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Worst-case behavior of simple sequencing rules in flow shop scheduling with general position-dependent Learning Effects
Annals of Operations Research, 2011Co-Authors: Ji-bo Wang, Ming-zheng WangAbstract:A real industrial production phenomenon, referred to as Learning Effects, has drawn increasing attention. However, most research on this issue considers only single machine problems. Motivated by this limitation, this paper considers flow shop scheduling problems with a general position-dependent Learning Effects. By the general position-dependent Learning Effects, we mean that the actual processing time of a job is defined by a general non-increasing function of its scheduled position. The objective is to minimize one of the five regular performance criteria, namely, the total completion time, the makespan, the total weighted completion time, the total weighted discounted completion time, and the sum of the quadratic job completion times. We present heuristic algorithms by using the optimal permutations for the corresponding single machine scheduling problems. We also analyze the worst-case bound of our heuristic algorithms.
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Single-machine group scheduling with both Learning Effects and deteriorating jobs
Computers & Industrial Engineering, 2011Co-Authors: Xue Huang, Ming-zheng Wang, Ji-bo WangAbstract:In the paper two resource constrained single-machine group scheduling problems with both Learning Effects and deteriorating jobs are considered. By Learning Effects, deteriorating jobs and group technology assumption, we mean that the processing time of a job is defined by the function of its starting time and position in the group, and the group setup times of a group is a positive strictly decreasing continuous function of the amount of consumed resource. We present polynomial solutions for the makespan minimization problem under the constraint that the total resource consumption does not exceed a given limit, and the total resource consumption minimization problem under the constraint that the makespan does not exceed a given limit, respectively.
Francois Cilliers - One of the best experts on this subject based on the ideXlab platform.
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Modelling the pre-assessment Learning Effects of assessment : evidence in the validity chain
Medical Education, 2012Co-Authors: Francois Cilliers, Cees P. M. Van Der VleutenAbstract:OBJECTIVES: We previously developed a model of the pre-assessment Learning Effects of consequential assessment and started to validate it. The model comprises assessment factors, mechanism factors and Learning Effects. The purpose of this study was to continue the validation process. For stringency, we focused on a subset of assessment factor-Learning effect associations that featured least commonly in a baseline qualitative study. Our aims were to determine whether these uncommon associations were operational in a broader but similar population to that in which the model was initially derived. METHODS: A cross-sectional survey of 361 senior medical students at one medical school was undertaken using a purpose-made questionnaire based on a grounded theory and comprising pairs of written situational tests. In each pair, the manifestation of an assessment factor was varied. The frequencies at which Learning Effects were selected were compared for each item pair, using an adjusted alpha to assign significance. The frequencies at which mechanism factors were selected were calculated. RESULTS: There were significant differences in the Learning effect selected between the two scenarios of an item pair for 13 of this subset of 21 uncommon associations, even when a p-value of < 0.00625 was considered to indicate significance. Three mechanism factors were operational in most scenarios: agency; response efficacy, and response value. CONCLUSIONS: For a subset of uncommon associations in the model, the role of most assessment factor-Learning effect associations and the mechanism factors involved were supported in a broader but similar population to that in which the model was derived. Although model validation is an ongoing process, these results move the model one step closer to the stage of usefully informing interventions. Results illustrate how factors not typically included in studies of the Learning Effects of assessment could confound the results of interventions aimed at using assessment to influence Learning.
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A model of the pre-assessment Learning Effects of assessment is operational in an undergraduate clinical context
BMC Medical Education, 2012Co-Authors: Francois Cilliers, Lambert Schuwirth, Cees P. M. Van Der VleutenAbstract:Background No validated model exists to explain the Learning Effects of assessment, a problem when designing and researching assessment for Learning. We recently developed a model explaining the pre-assessment Learning Effects of summative assessment in a theory teaching context. The challenge now is to validate this model. The purpose of this study was to explore whether the model was operational in a clinical context as a first step in this process.
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A Model of the Pre-assessment Learning Effects of Summative Assessment in Medical Education.
Advances in Health Sciences Education, 2011Co-Authors: Francois Cilliers, Nicoline Herman, Hanelie Adendorff, Cees P. M. Van Der VleutenAbstract:It has become axiomatic that assessment impacts powerfully on student Learning. However, surprisingly little research has been published emanating from authentic higher education settings about the nature and mechanism of the pre-assessment Learning Effects of summative assessment. Less still emanates from health sciences education settings. This study explored the pre-assessment Learning Effects of summative assessment in theoretical modules by exploring the variables at play in a multifaceted assessment system and the relationships between them. Using a grounded theory strategy, in-depth interviews were conducted with individual medical students and analyzed qualitatively. Respondents’ Learning was influenced by task demands and system design. Assessment impacted on respondents’ cognitive processing activities and metacognitive regulation activities. Individually, our findings confirm findings from other studies in disparate non-medical settings and identify some new factors at play in this setting. Taken together, findings from this study provide, for the first time, some insight into how a whole assessment system influences student Learning over time in a medical education setting. The findings from this authentic and complex setting paint a nuanced picture of how intricate and multifaceted interactions between various factors in an assessment system interact to influence student Learning. A model linking the sources, mechanism and consequences of the pre-assessment Learning Effects of summative assessment is proposed that could help enhance the use of summative assessment as a tool to augment Learning.