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We present one of the University of Kentucky TraceLab components, Similarity Matrix Voting Merge. We highlight some particularly interesting aspects of the component such as challenges faced when developing it. We discuss the challenges encountered when setting up unit testing for the component. We provide an example of the component being used in a TraceLab experiment. We provide a link for download of the component. 

We examine the effects of stemming on the tracing of software engineering artifacts. We compare two common stemming algorithms to each other as well as to a baseline of no stemming. We evaluate the algorithms on eight tracing datasets. We run the experiment using the TraceLab experimental framework to allow for ease of repeatability and knowledge sharing among the tracing community. We compare the algorithms on precision at recall levels of [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], as well as on mean average precision values. The experiment indicated that neither the Porter stemmer nor the Krovetz stemmer outperformed the other on all datasets tested. 

Traditional high performance computing (HPC) centers that operate a single large supercomputer cluster have not required sophisticated mechanisms to manage and enforce network policies. Recently, HPC centers have expanded to support a wide range of computational infrastructure, such as OpenStack-based private clouds and Ceph object stores, each with its own unique characteristics and network security requirements. Network security policies are becoming more complex and harder to manage. To address the challenge, this paper explores ways to define and manage the new network policies required by emerging HPC systems. As the first step, we identify the new types of policies that are required and the technical capabilities needed to support them. We present example policies and discuss ways to implement those policies using emerging programmable networks and intent-based networks. We describe our initial work toward automatically converting human readable network policies into network configurations and programmable network controllers that implement those policies using business rule management systems. 

Research must be reproducible in order to make an impact on science and to contribute to the body of knowledge in our field. Yet studies have shown that 70% of research from academic labs cannot be reproduced. In software engineering, and more specifically requirements engineering (RE), reproducible research is rare, with datasets not always available or methods not fully described. This lack of reproducible research hinders progress, with researchers having to replicate an experiment from scratch. A researcher starting out in RE has to sift through conference papers, finding ones that are empirical, then must look through the data available from the empirical paper (if any) to make a preliminary determination if the paper can be reproduced. This paper addresses two parts of that problem, identifying RE papers and identifying empirical papers within the RE papers. Recent RE and empirical conference papers were used to learn features and to build an automatic classifier to identify RE and empirical papers. We introduce the Empirical Requirements Research Classifier (ERRC) method, which uses natural language processing and machine learning to perform supervised classification of conference papers. We compare our method to a baseline keyword-based approach. To evaluate our approach, we examine sets of papers from the IEEE Requirements Engineering conference and the IEEE International Symposium on Software Testing and Analysis. We found that the ERRC method performed better than the baseline method in all but a few cases. 

[Context and motivation] Trace matrices are lynch pins for the development of mission- and safety-critical software systems and are useful for all software systems, yet automated methods for recovering trace links are far from perfect. This limitation makes the job of human analysts who must vet recovered trace links more difficult. [Question/Problem] Earlier studies suggested that certain analyst behaviors when performing trace recovery tasks lead to decreased accuracy of recovered trace relationships. We propose a three-step experimental study to: (a) determine if there really are behaviors that lead to errors of judgment for analysts, (b) enhance the requirements tracing software to curtail such behaviors, and (c) determine if curtailing such behaviors results in increased accuracy. [Principal ideas/results] We report on a preliminary study we undertook in which we modified the user interface of RETRO.NET to curtail two behaviors indicated by the earlier work. We report on observed results. [Contributions] We describe and discuss a major study of potentially unwanted analyst behaviors and present results of a preliminary study toward determining if curbing these behaviors with enhancements to tracing software leads to fewer human errors. 

Concurrent programs are difficult to test due to their inherent non-determinism. To address this problem, testing often requires the exploration of thread schedules of a program; this can be time-consuming when applied to real-world programs. Software defect prediction has been used to help developers find faults and prioritize their testing efforts. Prior studies have used machine learning to build such predicting models based on designed features that encode the characteristics of programs. However, research has focused on sequential programs; to date, no work has considered defect prediction for concurrent programs, with program characteristics distinguished from sequential programs. In this paper, we present ConPredictor, an approach to predict defects specific to concurrent programs by combining both static and dynamic program metrics. Specifically, we propose a set of novel static code metrics based on the unique properties of concurrent programs. We also leverage additional guidance from dynamic metrics constructed based on mutation analysis. Our evaluation on four large open source projects shows that ConPredictor improved both within-project defect prediction and cross-project defect prediction compared to traditional features.