Technique Integration for Requirements Assessment

In determining whether to permit a safety-critical software system to be certified and in performing independent verification and validation (IV&V) of safety- or mission-critical systems, the requirements traceability matrix (RTM) delivered by the developer must be assessed for accuracy. The current state of the practice is to perform this work manually, or with the help of general-purpose tools such as word processors and spreadsheets Such work is error-prone and person-power intensive. In this paper, we extend our prior work in application of Information Retrieval (IR) methods for candidate link generation to the problem of RTM accuracy assessment. We build voting committees from five IR methods, and use a variety of voting schemes to accept or reject links from given candidate RTMs. We report on the results of two experiments. In the first experiment, we used 25 candidate RTMs built by human analysts for a small tracing task involving a portion of a NASA scientific instrument specification. In the second experiment, we randomly seeded faults in the RTM for the entire specification. Results of the experiments are presented

Process improvement for traceability: A study of human fallibility

Abstract—Human analysts working with results from automated traceability tools often make incorrect decisions that lead to lower quality final trace matrices. As the human must vet the results of trace tools for mission- and safety-critical systems, the hopes of developing expedient and accurate tracing procedures lies in understanding how analysts work with trace matrices. This paper describes a study to understand when and why humans make correct and incorrect decisions during tracing tasks through logs of analyst actions. In addition to the traditional measures of recall and precision to describe the accuracy of the results, we introduce and study new measures that focus on analyst work quality: potential recall, sensitivity, and effort distribution. We use these measures to visualize analyst progress towards the final trace matrix, identifying factors that may influence their performance and determining how actual tracing strategies, derived from analyst logs, affect results

Transcranial Duplex Ultrasonography Measurements Towards Identification of Blood Vessel Conditions : Artificial Cerebral Blood Flow in Pathologies

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Nanoscale Electrical Potential and Roughness of a Calcium Phosphate Surface Promotes the Osteogenic Phenotype of Stromal Cells

Mesenchymal stem cells (MSCs) and osteoblasts respond to the surface electrical charge and topography of biomaterials. This work focuses on the connection between the roughness of calcium phosphate (CP) surfaces and their electrical potential (EP) at the micro- and nanoscales and the possible role of these parameters in jointly affecting human MSC osteogenic differentiation and maturation in vitro. A microarc CP coating was deposited on titanium substrates and characterized at the micro- and nanoscale. Human adult adipose-derived MSCs (hAMSCs) or prenatal stromal cells from the human lung (HLPSCs) were cultured on the CP surface to estimate MSC behavior. The roughness, nonuniform charge polarity, and EP of CP microarc coatings on a titanium substrate were shown to affect the osteogenic differentiation and maturation of hAMSCs and HLPSCs in vitro. The surface EP induced by the negative charge increased with increasing surface roughness at the microscale. The surface relief at the nanoscale had an impact on the sign of the EP. Negative electrical charges were mainly located within the micro- and nanosockets of the coating surface, whereas positive charges were detected predominantly at the nanorelief peaks. HLPSCs located in the sockets of the CP surface expressed the osteoblastic markers osteocalcin and alkaline phosphatase. The CP multilevel topography induced charge polarity and an EP and overall promoted the osteoblast phenotype of HLPSCs. The negative sign of the EP and its magnitude at the micro- and nanosockets might be sensitive factors that can trigger osteoblastic differentiation and maturation of human stromal cells

Will Johnny/Joanie Make a Good Software Engineer?: Are Course Grades Showing the Whole Picture?

Predicting future success of students as software engineers is an open research area. We posit that current grading means do not capture all the information that may predict whether students will become good software engineers. We use one such piece of information, traceability of project artifacts, to illustrate our argument. Traceability has been shown to be an indicator of software project quality in industry. We present the results of a case study of a University of Waterloo graduate-level software engineering course where traceability was examined as well as course grades (such as mid-term, project grade, etc.). We found no correlation between the presence of good traceability and any of the course grades, lending support to our argument

On Human Analyst Performance in Assisted Requirements Tracing: Statistical Analysis

Assisted requirements tracing is a process in which a human analyst validates candidate traces produced by an automated requirements tracing method or tool. The assisted requirements tracing process splits the difference between the commonly applied time-consuming, tedious, and error-prone manual tracing and the automated requirements tracing procedures that are a focal point of academic studies. In fact, in software assurance scenarios, assisted requirements tracing is the only way in which tracing can be at least partially automated. In this paper, we present the results of an extensive 12 month study of assisted tracing, conducted using three different tracing processes at two different sites. We describe the information collected about each study participant and their work on the tracing task, and apply statistical analysis to study which factors have the largest effect on the quality of the final trace

Will Johnny/Joanie Make a Good Software Engineer?: Are Course Grades Showing the Whole Picture?

Predicting future success of students as software engineers is an open research area. We posit that current grading means do not capture all the information that may predict whether students will become good software engineers. We use one such piece of information, traceability of project artifacts, to illustrate our argument. Traceability has been shown to be an indicator of software project quality in industry. We present the results of a case study of a University of Waterloo graduate-level software engineering course where traceability was examined as well as course grades (such as mid-term, project grade, etc.). We found no correlation between the presence of good traceability and any of the course grades, lending support to our argument. 1

Security of e-banking systems: modelling the process of counteracting e-banking fraud

The paper is devoted to the current issue of the counteracting cyberattacks in the banking sector, in particular in the field of e-banking. The main types of banking fraud, which are carried out in the online sphere, are considered. The authors propose a mathematical model that describes the process of counteracting e-banking fraud. Proposed model is based on the classic Lotka-Volterra model with logistic growth and the Holling-Tanner dynamic models. The fixed points of a dynamic system were calculated and analyzed. It was determined that there are 4 possible types of fixed points: saddle and the line of stable fixed points, which are unlikely may be in real life, stable node and a stable degenerate node, which are, in practice, the most likely cases. The constructed model could be used for theoretical study, different simulation experiments with changing input parameters could be done. Unfortunately, it is difficult to investigate this question on real data, since the statistics on cyberattacks are closed