Symbolic Model Checking of Product-Line Requirements Using SAT-Based Methods

Product line (PL) engineering promotes the de- velopment of families of related products, where individual products are differentiated by which optional features they include. Modelling and analyzing requirements models of PLs allows for early detection and correction of requirements errors – including unintended feature interactions, which are a serious problem in feature-rich systems. A key challenge in analyzing PL requirements is the efficient verification of the product family, given that the number of products is too large to be verified one at a time. Recently, it has been shown how the high-level design of an entire PL, that includes all possible products, can be compactly represented as a single model in the SMV language, and model checked using the NuSMV tool. The implementation in NuSMV uses BDDs, a method that has been outperformed by SAT-based algorithms. In this paper we develop PL model checking using two leading SAT-based symbolic model checking algorithms: IMC and IC3. We describe the algorithms, prove their correctness, and report on our implementation. Evaluating our methods on three PL models from the literature, we demonstrate an improvement of up to 3 orders of magnitude over the existing BDD-based method.NSERC Discovery Grant, 155243-12 || NSERC / Automotive Partnership Canada, APCPJ 386797 - 09 || Ontario Research Fund, RE05-04

Managing in an economic crisis: The role of market orientation in an international law firm

This research paper contributes to the understanding of the relationship between market orientation and performance in the context of a law firm during a time of economic crisis. The contribution is twofold, adding to the fairly limited research on market orientation within law firms, and to the limited research on the role of market orientation in times of economic crisis. The findings, from the questionnaire survey and semi-structured interviews within practice groups of a large multinational law firm, conclude that market orientation is important during an economic crisis. Those practice groups with higher market orientation scores withstand the increased turbulence and outperform those practice groups with lower market orientation scores

A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS

We present a noninvasive Web-based app to help exclude or diagnose myelodysplastic syndrome (MDS), a bone marrow (BM) disorder with cytopenias and leukemic risk, diagnosed by BM examination. A sample of 502 MDS patients from the European MDS (EUMDS) registry (n \gt; 2600) was combined with 502 controls (all BM proven). Gradient-boosted models (GBMs) were used to predict/exclude MDS using demographic, clinical, and laboratory variables. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the models, and performance was validated using 100 times fivefold cross-validation. Model stability was assessed by repeating its fit using different randomly chosen groups of 502 EUMDS cases. AUC was 0.96 (95\ 0.95-0.97). MDS is predicted/excluded accurately in 86\range, 0-1) of less than 0.68 (GBM \lt; 0.68) resulted in a negative predictive value of 0.94, that is, MDS was excluded. GBM ≥ 0.82 provided a positive predictive value of 0.88, that is, MDS. The diagnosis of the remaining patients (0.68 ≤ GBM \lt; 0.82) is indeterminate. The discriminating variables: age, sex, hemoglobin, white blood cells, platelets, mean corpuscular volume, neutrophils, monocytes, glucose, and creatinine. A Web-based app was developed; physicians could use it to exclude or predict MDS noninvasively in most patients without a BM examination. Future work will add peripheral blood cytogenetics/genetics, EUMDS-based prospective validation, and prognostication

PathFinder: A Tool for Design Exploration

In this paper we present a tool called PathFinder, which exploits the power of model checking for developing and debugging newly-written hardware designs. Our tool targets the community of design engineers, who -- in contrast to verification engineers -- are not versed in formal verification, and therefore have traditionally been distant from the growing industry momentum in the area of model checking..