AdaBoost approaches have been used for multi-class imbalance classification with an imbalance ratio measured on class sizes. However, such ratio would assign each training sample of the same class with the same weight, thus failing to reflect the data distribution within a class. We propose to incorporate the density information of training samples into the class imbalance ratio so that samples of the same class could have different weights. As one could use the entire training set to calculate the imbalance and density factors, the weight of a training sample resulting from the two factors remains static throughout the training epochs. However, static weights could not reflect the up-to-date training status of base learners. To deal with this, we propose to design an adaptive weighting mechanism by making use of up-to-date training status to further alleviate the multi-class imbalance issue. Ultimately, we incorporate the class imbalance ratio, the density-based factor, and the adaptive weighting mechanism into a single variable, based on which the adaptive weights of all training samples are computed. Experimental studies are carried out to investigate the effectiveness of the proposed approach and each of the three components in dealing with multi-class imbalance classification problem.

Just-In-Time Software Defect Prediction (JIT-SDP) uses machine learning to predict whether software changes are defect-inducing or clean. When adopting JIT-SDP, changes in the underlying defect generating process may significantly affect the predictive performance of JIT-SDP models over time. Therefore, being able to continuously track the predictive performance of JIT-SDP models during the software development process is of utmost importance for software companies to decide whether or not to trust the predictions provided by such models over time. However, there has been little discussion on how to continuously evaluate predictive performance in practice, and such evaluation is not straightforward. In particular, labeled software changes that can be used for evaluation arrive over time with a delay, which in part corresponds to the time we have to wait to label software changes as ‘clean’ (waiting time). A clean label assigned based on a given waiting time may not correspond to the true label of the software changes. This can potentially hinder the validity of any continuous predictive performance evaluation procedure for JIT-SDP models. This paper provides the first discussion of how to continuously evaluate predictive performance of JIT-SDP models over time during the software development process, and the first investigation of whether and to what extent waiting time affects the validity of such continuous performance evaluation procedure in JIT-SDP. Based on 13 GitHub projects, we found that waiting time had a significant impact on the validity. Though typically small, the differences in estimated predicted performance were sometimes large, and thus inappropriate choices of waiting time can lead to misleading estimations of predictive performance over time. Such impact did not normally change the ranking between JIT-SDP models, and thus conclusions in terms of which JIT-SDP model performs better are likely reliable independent of the choice of waiting time, especially when considered across projects.

Software effort estimation (SEE) usually suffers from inherent uncertainty arising from predictive model limitations and data noise. Relying on point estimation only may ignore the uncertain factors and lead project managers (PMs) to wrong decision making. Prediction intervals (PIs) with confidence levels (CLs) present a more reasonable representation of reality, potentially helping PMs to make better-informed decisions and enable more flexibility in these decisions. However, existing methods for PIs either have strong limitations or are unable to provide informative PIs. To develop a “better” effort predictor, we propose a novel PI estimator called Synthetic Bootstrap ensemble of Relevance Vector Machines (SynB-RVM) that adopts Bootstrap resampling to produce multiple RVM models based on modified training bags whose replicated data projects are replaced by their synthetic counterparts. We then provide three ways to assemble those RVM models into a final probabilistic effort predictor, from which PIs with CLs can be generated. When used as a point estimator, SynB-RVM can either significantly outperform or have similar performance compared with other investigated methods. When used as an uncertain predictor, SynB-RVM can achieve significantly narrower PIs compared to its base learner RVM. Its hit rates and relative widths are no worse than the other compared methods that can provide uncertain estimation.

Strong retardation of ultrashort optical pulses, including their deceleration and stoppage in the form of Bragg solitons in a cascaded Bragg grating (BG) structure, is proposed. The manipulations of the pulses are carried out, using nonlinear effects, in a chirped BG segment which is linked, via a defect, to a uniform grating. The storage of the ultrashort pulses is shown to be very robust with respect to variations of the input field intensity, suggesting the feasibility of storing ultrafast optical pulses in such a structure. Physical estimates are produced for the BGs written in silicon.

Three key challenges faced by the task of software effort estimation (SEE) when using predictive models are: (1) in order to support decision-making, software managers should have access not only to the effort estimation given by the predictive model, but also how confident this model is in estimating a given project and how likely other effort values could be the real efforts required to develop this project, (2) SEE data is likely to contain noise, due to the participation of humans in the data collection, and this noise can hinder predictions if not catered, and (3) data collection is an expensive task, and guidelines on when new data need to be collected would be helpful for reducing the cost associated with data collection. However, even though SEE has been studied for decades and many predictors have been proposed, few methods focus on these issues. In this work, we show that relevance vector machine (RVM) is a promising predictive method for addressing these three challenges. More specifically, it explicitly handles noise, it provides probabilistic predictions of effort, and can be used to identify when the required efforts of new projects should be collected for using them as training examples. With that in mind, this work provides the first step in exploiting RVM's potential for SEE by validating both its point prediction and prediction intervals. It then explains in detail future directions in terms of how RVMs can be further exploited for addressing the above mentioned challenges. Our systematic experiments show that RVM is very competitive compared with state-of-the-art SEE approaches, being usually ranked the first or second in 7 across 11 data sets in terms of mean absolute error. We also demonstrate how RVM can be used to judge the amount of noise present in the data. In summary, we show that RVM is a very promising predictor for SEE and should be further exploited.