Visible to the public Biblio

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2020-03-09
Song, Zekun, Wang, Yichen, Zong, Pengyang, Ren, Zhiwei, Qi, Di.  2019.  An Empirical Study of Comparison of Code Metric Aggregation Methods–on Embedded Software. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :114–119.

How to evaluate software reliability based on historical data of embedded software projects is one of the problems we have to face in practical engineering. Therefore, we establish a software reliability evaluation model based on code metrics. This evaluation technique requires the aggregation of software code metrics into project metrics. Statistical value methods, metric distribution methods, and econometric methods are commonly-used aggregation methods. What are the differences between these methods in the software reliability evaluation process, and which methods can improve the accuracy of the reliability assessment model we have established are our concerns. In view of these concerns, we conduct an empirical study on the application of software code metric aggregation methods based on actual projects. We find the distribution of code metrics for the projects under study. Using these distribution laws, we optimize the aggregation method of code metrics and improve the accuracy of the software reliability evaluation model.

2017-12-12
Nadgowda, S., Duri, S., Isci, C., Mann, V..  2017.  Columbus: Filesystem Tree Introspection for Software Discovery. 2017 IEEE International Conference on Cloud Engineering (IC2E). :67–74.

Software discovery is a key management function to ensure that systems are free of vulnerabilities, comply with licensing requirements, and support advanced search for systems containing given software. Today, software is predominantly discovered through querying package management tools, or using rules that check for file metadata or contents. These approaches are inadequate as not every software is installed through package managers, and agile development practices lead to frequent deployment of software. Other approaches to software discovery use machine learning methods requiring training phase, or require maintaining knowledge bases. Columbus uses the knowledge of the software packaging practices that evolved over time, and uses the information embedded in the file system impression created by a software package to discover it. Columbus is able to discover software in 92% of all official Docker images. Further, Columbus can be used in problem diagnosis and drift detection situations to compare two different systems, or to determine the evolution of a system overtime.

2017-03-07
Rashid, A., Moore, K., May-Chahal, C., Chitchyan, R..  2015.  Managing Emergent Ethical Concerns for Software Engineering in Society. 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering. 2:523–526.

This paper presents an initial framework for managing emergent ethical concerns during software engineering in society projects. We argue that such emergent considerations can neither be framed as absolute rules about how to act in relation to fixed and measurable conditions. Nor can they be addressed by simply framing them as non-functional requirements to be satisficed. Instead, a continuous process is needed that accepts the 'messiness' of social life and social research, seeks to understand complexity (rather than seek clarity), demands collective (not just individual) responsibility and focuses on dialogue over solutions. The framework has been derived based on retrospective analysis of ethical considerations in four software engineering in society projects in three different domains.