Variationally consistent computational homogenization of chemomechanical problems with stabilized weakly periodic boundary conditions

A variationally consistent model-based computational homogenization approach for transient chemomechanically coupled problems is developed based on the classical assumption of first-order prolongation of the displacement, chemical potential, and (ion) concentration fields within a representative volume element (RVE). The presence of the chemical potential and the concentration as primary global fields represents a mixed formulation, which has definite advantages. Nonstandard diffusion, governed by a Cahn–Hilliard type of gradient model, is considered under the restriction of miscibility. Weakly periodic boundary conditions on the pertinent fields provide the general variational setting for the uniquely solvable RVE-problem(s). These boundary conditions are introduced with a novel approach in order to control the stability of the boundary discretization, thereby circumventing the need to satisfy the LBB-condition: the penalty stabilized Lagrange multiplier formulation, which enforces stability at the cost of an additional Lagrange multiplier for each weakly periodic field (three fields for the current problem). In particular, a neat result is that the classical Neumann boundary condition is obtained when the penalty becomes very large. In the numerical examples, we investigate the following characteristics: the mesh convergence for different boundary approximations, the sensitivity for the choice of penalty parameter, and the influence of RVE-size on the macroscopic response

Bug Triaging with High Confidence Predictions

Correctly assigning bugs to the right developer or team, i.e., bug triaging, is a costly activity. A concerted effort at Ericsson has been done to adopt automated bug triaging to reduce development costs. We also perform a case study on Eclipse bug reports. In this work, we replicate the research approaches that have been widely used in the literature including FixerCache. We apply them on over 10k bug reports for 9 large products at Ericsson and 2 large Eclipse products containing 21 components. We find that a logistic regression classifier including simple textual and categorical attributes of the bug reports has the highest accuracy of 79.00% and 46% on Ericsson and Eclipse bug reports respectively. Ericsson’s bug reports often contain logs that have crash dumps and alarms. We add this information to the bug triage models. We find that this information does not improve the accuracy of bug triaging in Ericsson’s context. Eclipse bug reports contain the stack traces that we add to the bug triaging model. Stack traces are only present in 8% of bug reports and do not improve the triage accuracy. Although our models perform as well as the best ones reported in the literature, a criticism of bug triaging at Ericsson is that accuracy is not sufficient for regular use. We develop a novel approach that only triages bugs when the model has high confidence in the triage prediction. We find that we improve the accuracy to 90% at Ericsson and 70% at Eclipse, but we can make predictions for 62% and 25% of the total Ericsson and Eclipse bug reports,respectively

Intelligent Software Bugs Localization, Triage and Prioritization

One of the time-consuming software maintenance tasks is the localization of software bugs especially in large systems. Developers have to follow a tedious process to reproduce the abnormal behavior then inspect a large number of files in order to resolve the bugs. Furthermore, software developers are usually overwhelmed with several reports of critical bugs to be addressed urgently and simultaneously. The management of these bugs is a complex problem due to the limited resources and the deadlines-pressure. Another critical task in this process is to assign appropriate priority to the bugs and eventually assign them to the right developers for resolution. Several studies have been proposed for bugs localization, the majority of them are recommending classes as outputs which may still require high inspection effort. In addition, there is a significant difference between the natural language used in bug reports and the programming language which limits the efficiency of existing approaches since most of them are mainly based on lexical similarity. Most of the existing studies treated bug reports in isolation when assigning them to developers. They also lack the understanding of dynamics of changing bug priorities. Thus, developers may spend considerable cognitive efforts moving between completely unrelated bug reports. To address these challenges, we proposed the following research contributions: 1. We proposed an automated approach to find and rank the potential classes and methods in order to localize software defects. Our approach finds a good balance between minimizing the number of recommended classes and maximizing the relevance of the proposed solution using a hybrid multi-objective optimization algorithm combining local and global search. Our hybrid multi-objective approach is able to successfully locate the true buggy methods within the top 10 recommendations for over 78% of the bug reports leading to a significant reduction of developers' effort comparing to class-level bug localization techniques. 2. We proposed an automated bugs triage approach based on the dependencies between several open bug reports. We defined the dependency between two bug reports as the number of common files to be inspected to localize the bugs. Then, we adopted multi-objective search to rank the bug reports for programmers. The results show a significant time reduction of over 30% in localizing the bugs simultaneously comparing to the traditional bugs prioritization technique based on only priorities. 3. We performed an empirical study to observe and understand the changes in bugs' priority in order to build a 3-W model on Why and When bug priorities change, and Who performs the change. We conducted interviews and a survey with practitioners as well as performed a quantitative analysis large database of bugs reports. As a result, we observed frequent changes in bug priorities and their impact on delaying critical bug fixes especially before shipping a new release.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/170906/1/Rafi Almhana Final Dissertation.pdfDescription of Rafi Almhana Final Dissertation.pdf : Dissertatio