On the Viability of Quantitative Assessment Methods in Software Engineering and Software Services

IT help desk operations are expensive. Costs associated with IT operations present challenges to profit goals. Help desk managers need a way to plan staffing levels so that labor costs are minimized while problems are resolved efficiently. An incident prediction method is needed for planning staffing levels. The potential value of a solution to this problem is important to an IT service provider since software failures are inevitable and their timing is difficult to predict. In this research, a cost model for help desk operations is developed. The cost model relates predicted incidents to labor costs using real help desk data. Incidents are predicted using software reliability growth models. Cluster analysis is used to group products with similar help desk incident characteristics. Principal Components Analysis is used to determine one product per cluster for the prediction of incidents for all members of the cluster. Incident prediction accuracy is demonstrated using cluster representatives, and is done so successfully for all clusters with accuracy comparable to making predictions for each product in the portfolio. Linear regression is used with cost data for the resolution of incidents to relate incident predictions to help desk labor costs. Following a series of four pilot studies, the cost model is validated by successfully demonstrating cost prediction accuracy for one month prediction intervals over a 22 month period

A New Fast and Efficient Conformal Mapping Based Technique for Remote Sensing Data Compression and Transmittal

In this paper we deal with a new technique for large data compression. Contour mapping of two dimensional objects is of fundamental importance in remote sensing and computer vision applications. We present extensive algorithms applied to polygonized, simply-connected contours and reproduce desired shapes using an innovative data compression technique based on conformal mapping. In a previous work3,4, through a conformal mapping process, we demonstrated the ability to 1) recognize shapes, and 2) concisely represent shape boundaries using a set of polynomial coefficients derived in the mapping process. In this work we illustrate how these previous results can be applied to data compression. In particular, in the approach outlined herein, a syntactic representation is formed for polygon shapes whose representation we desire to extract and reproduce compactly. Additionally, we present a problem of concavity in shape boundaries and a proposed solution in which polygons are divided into convex subsets and reconstructed accordingly

The genomic landscape of balanced cytogenetic abnormalities associated with human congenital anomalies

Despite the clinical significance of balanced chromosomal abnormalities (BCAs), their characterization has largely been restricted to cytogenetic resolution. We explored the landscape of BCAs at nucleotide resolution in 273 subjects with a spectrum of congenital anomalies. Whole-genome sequencing revised 93% of karyotypes and demonstrated complexity that was cryptic to karyotyping in 21% of BCAs, highlighting the limitations of conventional cytogenetic approaches. At least 33.9% of BCAs resulted in gene disruption that likely contributed to the developmental phenotype, 5.2% were associated with pathogenic genomic imbalances, and 7.3% disrupted topologically associated domains (TADs) encompassing known syndromic loci. Remarkably, BCA breakpoints in eight subjects altered a single TAD encompassing MEF2C, a known driver of 5q14.3 microdeletion syndrome, resulting in decreased MEF2C expression. We propose that sequence-level resolution dramatically improves prediction of clinical outcomes for balanced rearrangements and provides insight into new pathogenic mechanisms, such as altered regulation due to changes in chromosome topology

Exome sequencing of individuals with Huntington’s disease implicates FAN1 nuclease activity in slowing CAG expansion and disease onset

The age at onset of motor symptoms in Huntington’s disease (HD) is driven by HTT CAG repeat length but modified by other genes. In this study, we used exome sequencing of 683 patients with HD with extremes of onset or phenotype relative to CAG length to identify rare variants associated with clinical effect. We discovered damaging coding variants in candidate modifier genes identified in previous genome-wide association studies associated with altered HD onset or severity. Variants in FAN1 clustered in its DNA-binding and nuclease domains and were associated predominantly with earlier-onset HD. Nuclease activities of purified variants in vitro correlated with residual age at motor onset of HD. Mutating endogenous FAN1 to a nuclease-inactive form in an induced pluripotent stem cell model of HD led to rates of CAG expansion similar to those observed with complete FAN1 knockout. Together, these data implicate FAN1 nuclease activity in slowing somatic repeat expansion and hence onset of HD

Towards Better Help Desk Planning: Predicting Incidents and Required Effort

In this case study, a cost model for help desk operations is developed. The cost model relates predicted incidents to labor costs. Since incident estimation for hundreds of products is time-consuming, we use cluster analysis to group similarly behaving products in clusters, for which we then estimate incidents based on the representative product in the cluster. Incidents are predicted using software reliability growth models. The cost to resolve the incidents is predicted using historical labor data for the resolution of incidents. Cluster analysis is used to group products with similar help desk incident characteristics. We use Principal Components Analysis to determine one product per cluster for the prediction of incidents for all members of the cluster, so as to reduce estimation cost. We were able to predict incidents for a cluster based on this product alone and do so successfully for all clusters with accuracy comparable to making predictions for each product in the portfolio. Linear regression is used with cost data for the resolution of incidents to relate incident predictions to help desk labor costs. The cost model is then validated by successfully demonstrating cost prediction accuracy for one month prediction intervals over a 22 month period

Towards Better Help Desk Planning: Predicting Incidents and Required Effort

In this case study, a cost model for help desk operations is developed. The cost model relates predicted incidents to labor costs. Since incident estimation for hundreds of products is time-consuming, we use cluster analysis to group similarly behaving products in clusters, for which we then estimate incidents based on the representative product in the cluster. Incidents are predicted using software reliability growth models. The cost to resolve the incidents is predicted using historical labor data for the resolution of incidents. Cluster analysis is used to group products with similar help desk incident characteristics. We use Principal Components Analysis to determine one product per cluster for the prediction of incidents for all members of the cluster, so as to reduce estimation cost. We were able to predict incidents for a cluster based on this product alone and do so successfully for all clusters with accuracy comparable to making predictions for each product in the portfolio. Linear regression is used with cost data for the resolution of incidents to relate incident predictions to help desk labor costs. The cost model is then validated by successfully demonstrating cost prediction accuracy for one month prediction intervals over a 22 month period

Towards better help desk planning: Predicting incidents and required effort

In this case study, a cost model for help desk operations is developed. The cost model relates predicted incidents to labor costs. Since incident estimation for hundreds of products is time-consuming, we use cluster analysis to group similarly behaving products in clusters, for which we then estimate incidents based on the representative product in the cluster. Incidents are predicted using software reliability growth models. The cost to resolve the incidents is predicted using historical labor data for the resolution of incidents. Cluster analysis is used to group products with similar help desk incident characteristics. We use Principal Components Analysis to determine one product per cluster for the prediction of incidents for all members of the cluster, so as to reduce estimation cost. We were able to predict incidents for a cluster based on this product alone and do so successfully for all clusters with accuracy comparable to making predictions for each product in the portfolio. Linear regression is used with cost data for the resolution of incidents to relate incident predictions to help desk labor costs. The cost model is then validated by successfully demonstrating cost prediction accuracy for one month prediction intervals over a 22 month period