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Table 3. Per automatic learning tool Simple and Quality index results

in ABSTRACT A Machine Learning Toolkit for Power Systems Security Analysis
by unknown authors

Table 3. Per automatic learning tool Simple and Quality index results

in A Machine Learning Toolkit for Power Systems Security Analysis
by Dimitrios Semitekos Nikolaos

Table 3. User satisfaction with automatic correction tools as stated in user questionnaire

in Strategies
by unknown authors
"... In PAGE 6: ...tres, as reflected in Table3 . Despite the apparently good scores (in a scale from 0 to 10, were 10 is very good), when reading qualitative user impressions, one could detect that a few of them were unhappy with the most complex correction strategies, specially with the con- tent checking facilities (of course they did not know which strategy was used where, but they indicated in which exercises they were not so happy with the feedback).... ..."

Table 8: Effort saved by employing automatic code inspection tools.

in Should You Test the Code Before You Test the Program?
by Keith Pat On, Keith Paton
"... In PAGE 6: ...) The values of d and f are likely to vary from place to place. Table8 shows the value of dfic for i=0.... In PAGE 6: ...development can use this table to decide whether to go further with the analysis. Table8 shows effort saved as a percentage. Let us now assume that the loaded cost of a software developer is 150,000 US dollars per annum and each developer spends 50% of his/her effort in writing and debugging code.... ..."

(Table 2). In practice, an important issue is whether the checks and verifications proposed can be done automatically by a tool.

in Behavior Protocols
by Plasil Frantisek, Stanislav Visnovsky, Miloslav Besta

(Table 2). In practice, an important issue is whether the checks and verifications proposed can be done automatically by a tool.

in Behavior Protocols
by Frantisek Plasil, Stanislav Visnovsky, Miloslav Besta

(Table 2). In practice, an important issue is whether the checks and verifications proposed can be done automatically by a tool.

in Behavior Protocols Revised on 7/8/2001
by Plasil Frantisek, Stanislav Visnovsky, Miloslav Besta

Table 3. Application amp; Annotation Characteristics. This table shows, for each program, the variables that were automatically annotated, and the selection tool annotation time. The variables in bold are those that Calpa annotated, but the human missed.

in Calpa: A Tool for Automating Selective Dynamic Compilation
by Markus Mock, Craig Chambers, Susan J. Eggers 2000
Cited by 34

Table 3. Application amp; Annotation Characteristics. This table shows, for each program, the variables that were automatically annotated, and the selection tool annotation time. The variables in bold are those that Calpa annotated, but the human missed.

in Calpa: A Tool for Automating Selective Dynamic Compilation
by unknown authors

Table 2: CEP Simulation Speed Results For this design, we experimented with various bit widths for the data path. We only had to write the convolution encoding program in assembly once, since our tools automatically adjust for bit width changes if possible. The results of the experiment are shown in

in Fast Cycle-Accurate Simulation and Instruction Set Generation for Constraint-Based Descriptions of Programmable Architecturs
by Scott J. Weber, Matthew W. Moskewicz, Matthias Gries, Christian Sauer, Kurt Keutzer
"... In PAGE 5: ... We only had to write the convolution encoding program in assembly once, since our tools automatically adjust for bit width changes if possible. The results of the experiment are shown in Table2 . The results are reported for 2 billion simulated cycles.... ..."
Cited by 1
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