JITTAC: a just-in-time tool for architectural consistency

Architectural drift is a widely cited problem in software engineering, where the implementation of a software system diverges from the designed architecture over time causing architecture inconsistencies. Previous work suggests that this architectural drift is, in part, due to programmers’ lack of architecture awareness as they develop code. JITTAC is a tool that uses a real-time Reflexion Modeling approach to inform programmers of the architectural consequences of their programming actions as, and often just before, they perform them. Thus, it provides developers with Just-In-Time architectural awareness towards promoting consistency between the as-designed architecture and the as-implemented system. JITTAC also allows programmers to give real-time feedback on introduced inconsistencies to the architect. This facilitates programmer-driven architectural change, when validated by the architect, and allows for more timely team-awareness of the actual architectural consistency of the system. Thus, it is anticipated that the tool will decrease architectural inconsistency over time and improve both developers’ and architect's knowledge of their software’s architecture. The JITTAC demo is available at: http://www.youtube.com/watch?v=BNqhp40PDD

Identification of similar regions of protein structures using integrated sequence and structure analysis tools-2

<p><b>Copyright information:</b></p><p>Taken from "Identification of similar regions of protein structures using integrated sequence and structure analysis tools"</p><p>BMC Structural Biology 2006;6():4-4.</p><p>Published online 9 Mar 2006</p><p>PMCID:PMC1435900.</p><p>Copyright © 2006 Peters et al; licensee BioMed Central Ltd.</p> random member was selected. Then each representative was run against the method using the database specified in the legend, with the HMMer results excluded. The same set was run against ASTRAL 95 v1.67, if the domain was not found in v1.67, another random member of that fold was chosen, if the fold was not found in v1.67, it was excluded. The sensitivity and precision were measured as (true positives/total positives) and (true positives/total hits), respectively. EC.3 is the first three numbers of the EC number and SCOP SF is the first three numbers of the SCOP ID (superfamily)

Identification of similar regions of protein structures using integrated sequence and structure analysis tools-0

<p><b>Copyright information:</b></p><p>Taken from "Identification of similar regions of protein structures using integrated sequence and structure analysis tools"</p><p>BMC Structural Biology 2006;6():4-4.</p><p>Published online 9 Mar 2006</p><p>PMCID:PMC1435900.</p><p>Copyright © 2006 Peters et al; licensee BioMed Central Ltd.</p>windows illustrating both the query and the selected hit. For each hit, the Z-score, the PSI-BLAST e-value, the SCOP ID, the GO annotations and the EC number are displayed, if available. B) The function prediction page of 1DSU:A, showing how close each residue environment is to the annotation of the SCOP family b.47.1.2, trypsin fold serine proteases and how conserved the residues are in the PSI-BLAST PSSM

Genes up or down regulated 3 fold in control and <i>SNCA</i> triplication dopaminergic cultures after MPP treatment.

<p>Genes up or down regulated 3 fold in control and <i>SNCA</i> triplication dopaminergic cultures after MPP treatment.</p

Pluripotency verification of PD patient iPSC.

<p>A: Pluripotency score for iPSC lines A6, B119, I3, K20, P1, S110, T101,L1 and Y09. ESC H9 line served as a positive control, whereas fibroblasts from the healthy subject (Y line) were used as a negative control. Pluripotency range is depicted in red, whereas non-pluripotent range is shown in blue. B: Novelty score for tested samples. Low novelty score (green bars) is characteristic for pluripotent cell lines, whereas high novelty sore (red) highlights sample that deviated from the pluripotent transcriptional signature. C: Hierarchical clustering of all samples. D: Combination of pluripotency and novelty scores illustrates that iPSC and ESC samples are grouped together (red background—high pluripotency and low novelty scores). Y fibroblast line had the opposite result (blue background—low pluripotency and high novelty scores).</p

Microarray gene expression data quality control.

<p>A: the number of genes detected at p-value < 0.05 (red line) and p-value < 0.01 (blue line). Detection p-value is a measurement of confidence that a given transcript is expressed above the background level. B: Sample quality assessment by comparison of 95^th signal intensity values (red line) and signal-to-noise ratio (blue line) across samples. Signal-to-noise ratio is calculated as a ration of 95^th and 5^th percentile (p95/p05) in non-normalized data. C: Hierarchical clustering of samples after normalization and averaging of biological replicates.</p

Gene up or down regulated in <i>SNCA</i> triplication dopaminergic cultures relative to healthy control.

<p>Gene up or down regulated in <i>SNCA</i> triplication dopaminergic cultures relative to healthy control.</p

Integration-free iPSC lines generated from PD patient and control fibroblasts.

<p>Integration-free iPSC lines generated from PD patient and control fibroblasts.</p

Genes up or down regulated in all PD-patient specific dopaminergic cultures relative to control.

<p>Genes up or down regulated in all PD-patient specific dopaminergic cultures relative to control.</p

Mitogenic genes up or down regulated 3 fold in <i>SNCA</i> triplication dopaminergic cultures relative to control.

<p>Mitogenic genes up or down regulated 3 fold in <i>SNCA</i> triplication dopaminergic cultures relative to control.</p