Introduction

This page presents how SVSKit do cope efficiently with the CTLFireability examination face to the other participating tools. In this page, we consider «Surprise» models.

The next sections will show chart comparing performances in terms of both memory and execution time.The x-axis corresponds to the challenging tool where the y-axes represents SVSKit' performances. Thus, points below the diagonal of a chart denote comparisons favorables to the tool while others corresponds to situations where the challenging tool performs better.

You might also find plots out of the range that denote the case were at least one tool could not answer appropriately (error, time-out, could not compute or did not competed).

SVSKit versus GreatSPN+red

Some statistics are displayed below, based on 208 runs (104 for SVSKit and 104 for GreatSPN+red, so there are 104 plots on each of the two charts). Each execution was allowed 1 hour and 16 GByte of memory. Then performance charts comparing SVSKit to GreatSPN+red are shown (you may click on one graph to enlarge it).

On the chart below, denote cases where the two tools did computed all results without error, denote cases where the two tool did computed the same number of values (but not al values in the examination), denote cases where SVSKit computed more values than GreatSPN+red, denote cases where SVSKit computed less values than GreatSPN+red, denote the cases where at least one tool did not competed, denote the cases where at least one tool computed a bad value and denote the cases where at least one tool stated it could not compute a result or timed-out.

SVSKit wins when points are below the diagonal, GreatSPN+red wins when points are above the diagonal.

SVSKit versus LTSMin+red

Some statistics are displayed below, based on 208 runs (104 for SVSKit and 104 for LTSMin+red, so there are 104 plots on each of the two charts). Each execution was allowed 1 hour and 16 GByte of memory. Then performance charts comparing SVSKit to LTSMin+red are shown (you may click on one graph to enlarge it).

On the chart below, denote cases where the two tools did computed all results without error, denote cases where the two tool did computed the same number of values (but not al values in the examination), denote cases where SVSKit computed more values than LTSMin+red, denote cases where SVSKit computed less values than LTSMin+red, denote the cases where at least one tool did not competed, denote the cases where at least one tool computed a bad value and denote the cases where at least one tool stated it could not compute a result or timed-out.

SVSKit wins when points are below the diagonal, LTSMin+red wins when points are above the diagonal.

SVSKit versus ITS-Tools

Some statistics are displayed below, based on 208 runs (104 for SVSKit and 104 for ITS-Tools, so there are 104 plots on each of the two charts). Each execution was allowed 1 hour and 16 GByte of memory. Then performance charts comparing SVSKit to ITS-Tools are shown (you may click on one graph to enlarge it).

On the chart below, denote cases where the two tools did computed all results without error, denote cases where the two tool did computed the same number of values (but not al values in the examination), denote cases where SVSKit computed more values than ITS-Tools, denote cases where SVSKit computed less values than ITS-Tools, denote the cases where at least one tool did not competed, denote the cases where at least one tool computed a bad value and denote the cases where at least one tool stated it could not compute a result or timed-out.

SVSKit wins when points are below the diagonal, ITS-Tools wins when points are above the diagonal.

SVSKit versus LoLA

Some statistics are displayed below, based on 208 runs (104 for SVSKit and 104 for LoLA, so there are 104 plots on each of the two charts). Each execution was allowed 1 hour and 16 GByte of memory. Then performance charts comparing SVSKit to LoLA are shown (you may click on one graph to enlarge it).

On the chart below, denote cases where the two tools did computed all results without error, denote cases where the two tool did computed the same number of values (but not al values in the examination), denote cases where SVSKit computed more values than LoLA, denote cases where SVSKit computed less values than LoLA, denote the cases where at least one tool did not competed, denote the cases where at least one tool computed a bad value and denote the cases where at least one tool stated it could not compute a result or timed-out.

SVSKit wins when points are below the diagonal, LoLA wins when points are above the diagonal.

SVSKit versus Tapaal

Some statistics are displayed below, based on 208 runs (104 for SVSKit and 104 for Tapaal, so there are 104 plots on each of the two charts). Each execution was allowed 1 hour and 16 GByte of memory. Then performance charts comparing SVSKit to Tapaal are shown (you may click on one graph to enlarge it).

On the chart below, denote cases where the two tools did computed all results without error, denote cases where the two tool did computed the same number of values (but not al values in the examination), denote cases where SVSKit computed more values than Tapaal, denote cases where SVSKit computed less values than Tapaal, denote the cases where at least one tool did not competed, denote the cases where at least one tool computed a bad value and denote the cases where at least one tool stated it could not compute a result or timed-out.

SVSKit wins when points are below the diagonal, Tapaal wins when points are above the diagonal.

SVSKit versus 2023-gold

Some statistics are displayed below, based on 208 runs (104 for SVSKit and 104 for 2023-gold, so there are 104 plots on each of the two charts). Each execution was allowed 1 hour and 16 GByte of memory. Then performance charts comparing SVSKit to 2023-gold are shown (you may click on one graph to enlarge it).

On the chart below, denote cases where the two tools did computed all results without error, denote cases where the two tool did computed the same number of values (but not al values in the examination), denote cases where SVSKit computed more values than 2023-gold, denote cases where SVSKit computed less values than 2023-gold, denote the cases where at least one tool did not competed, denote the cases where at least one tool computed a bad value and denote the cases where at least one tool stated it could not compute a result or timed-out.

SVSKit wins when points are below the diagonal, 2023-gold wins when points are above the diagonal.

SVSKit versus BVT-2024

Some statistics are displayed below, based on 208 runs (104 for SVSKit and 104 for BVT-2024, so there are 104 plots on each of the two charts). Each execution was allowed 1 hour and 16 GByte of memory. Then performance charts comparing SVSKit to BVT-2024 are shown (you may click on one graph to enlarge it).

Important: here, SVSKit is compared to BVT-2024. It is a good way to check how SVSKit compete in terms of resource consomption with the best tools (even virtual). When SVSKit is best, the corresponding plots are on the diagonal of the scatter plots chart.

On the chart below, denote cases where the two tools did computed all results without error, denote cases where the two tool did computed the same number of values (but not al values in the examination), denote cases where SVSKit computed more values than BVT-2024, denote cases where SVSKit computed less values than BVT-2024, denote the cases where at least one tool did not competed, denote the cases where at least one tool computed a bad value and denote the cases where at least one tool stated it could not compute a result or timed-out.

SVSKit wins when points are below the diagonal, BVT-2024 wins when points are above the diagonal.

SVSKit versus GreatSPN

Some statistics are displayed below, based on 208 runs (104 for SVSKit and 104 for GreatSPN, so there are 104 plots on each of the two charts). Each execution was allowed 1 hour and 16 GByte of memory. Then performance charts comparing SVSKit to GreatSPN are shown (you may click on one graph to enlarge it).

On the chart below, denote cases where the two tools did computed all results without error, denote cases where the two tool did computed the same number of values (but not al values in the examination), denote cases where SVSKit computed more values than GreatSPN, denote cases where SVSKit computed less values than GreatSPN, denote the cases where at least one tool did not competed, denote the cases where at least one tool computed a bad value and denote the cases where at least one tool stated it could not compute a result or timed-out.

SVSKit wins when points are below the diagonal, GreatSPN wins when points are above the diagonal.