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Table 1.1: Searches for language name AND either code OR language OR program in order to get meaningful results from searches for C and Python , both of which have other meanings. http://www.google.com accessed 3/6/2002.
2002
Table 3. Derived properties are obtained using the order relationship inherited from the implementation. Only for element containment and data edges the or- der is meaningful in the data model. Table 4 presents the derived properties of edges.
"... In PAGE 2: ... Table3 : Basic properties for edges derived property result next following sibling edge previous preceding sibling edge Table 4: Derived properties for E and D edges The name of the edge is the element name (edge of type E), the attribute name (edge of type A), the ID attribute name (edge of type R), or the string data (edge of type D) that it represents. E and D edges having the same parent are ordered in a list by the position in which they appear in the XML document.... ..."
Table 5: 3-Order Mutants The results shown in Table 5 indicates that the strength of the mutation coupling e ect increases with 3-order mutants. This is strong evidence that as the number of faults in a program that contribute to a failure increases, our test data is more likely to detect the failures. 5 CONCLUSIONS This paper presents several results about the coupling e ect as measured over the domain of mutation analysis. First, we found that test data developed to kill 1-order mutants are very successful at killing 2-order mutants. Of course, the fact that two mutants are executed on the same path does not necessarily mean that they interact in any meaningful way, but such interactions 17
1992
Cited by 53
Table 1 summarizes the effort used and planned for the period. A revised workplan and a reallocation of person-months was included in the contract amendment requesting a 6 months extension. Table 1 refers to the estimates in this revised workplan, in order to have a more meaningful comparison. Appendix B gives a more detailed report of the effort undertaken by each partner. The main discrepancies between actual and planned effort for the reporting period are due to extra work on WP4 and WP5 done during the first two months of the extension (2005-01-01/2005-02-28), in order to complete some deliverables submitted in month 38 and described in PPR3, and underestimation of the work in WP6 because of the extension.
Table 1: Benchmark numbers in order to determine the scaling ratio estimation (the unit is not meaningful, Alpha is given as 100 for reference) Notice that an approach like the one used in [17] allows more accuracy but is beyond the scope of our approach. 6The only limitation is that some timings e ects due to processor speci c strengths will not be re ected in the simulation (for instance cache size). 7Of course for a given processor, di erent processor clock rates will give di erent ratios and other benchmarks can be used, such as SpecInt and SpecFp.
1996
Cited by 1
Table 7 Necessary system knowledge for each of the 24 individualization cate gories.
1991
"... In PAGE 28: ... The necessary knowledge for user-tailoring a dialog system can be analyzed in more detail using the matrix classification scheme. In Table7 the necessary system knowledge is listed for each of the 24 individualization categories. Table 7 shows that method M1 (selectable alternatives) requires no special know- ledge about indivdualization procedures and is therefore most suitable for casual users, as has already been claimed.... In PAGE 28: ... In Table 7 the necessary system knowledge is listed for each of the 24 individualization categories. Table7 shows that method M1 (selectable alternatives) requires no special know- ledge about indivdualization procedures and is therefore most suitable for casual users, as has already been claimed. More interesting is the distinction between method M3 and M4 (configuration program/configuration file), since these methods offer the same possibilities (a high degree of freedom), but require different know- ledge.... In PAGE 28: ... More interesting is the distinction between method M3 and M4 (configuration program/configuration file), since these methods offer the same possibilities (a high degree of freedom), but require different know- ledge. Table7 shows that a special syntax in addition to the use of a text editor must be known for the configuration file method M4. Method M3 requires only knowledge about a special configuration program and will therefore be easier to understand.... In PAGE 29: ... Here user-tailoring becomes a dominant part of the user apos;s job and will be carried out regularly. In this case (see Table7 ) the... ..."
Cited by 16
Table 1: Meaningful Engagement Matrix
Table 4. Note that there are two semantic functions that give meaning to assertions: assertions are viewed either as Boolean expressions whose value is determined by the meaning of the special symbol and a state; or as descriptions of sets of partial functions on states. The latter view is in compliance with the GDM ideology: speci cations are sets and to satisfy a speci cation means to belong to the set. Most of the semantics is standard and obvious. Notice that the order of pairs ei=xi in a [e1=x1; : : :; en=xn] is not semantically meaningful since all the expressions ei are evaluated with the same state st (cf. the semantic restriction from Table 3 that the variables x1; : : :; xn are
"... In PAGE 6: ...[[: : :]] : Expr ! Sta ! V [[: : :]] : Bool ! Sta ! fT; Fg [[: : :]] : Prog ! Sta ~ ! Sta A[[: : :]] : Asrt ! (Sta ! fT; Fg) ! (Sta ! fT; Fg) S[[: : :]] : Asrt ! P(Sta ~ ! Sta) [[: : :]] : Sats ! fT; Fg Table4 : Semantics of the system. [[x]]st def= st x [[(fn e1 : : :en)]]st def= [[fn]] ([[e1]] st) : : :([[en]] st) [[(rn e1 : : :en)]]st def= [[rn]] ([[e1]] st) : : :([[en]] st) [[(: b)]] st def= IF [[b]] st = T THEN F ELSE T [[(b1 amp; b2)]] st def= IF [[b1]] st = T THEN [[b2]] st ELSE F [[(b1 _ b2)]] st def= IF [[b1]] st = T THEN T ELSE [[b2]] st [[skip]] st def= st [[x := e]] st def= st [x [[e]] st] [[p1; p2]] st def= IF [[p1]] st de ned THEN [[p2]]([[p1]] st) ELSE unde ned [[if b then p1 else p2 ]] st def= IF [[b]] st = T THEN [[p1]] st ELSE [[p2]] st [[while b do p od]] st def= IF [[b]] st = T THEN [[p; while b do p od]] st ELSE st A[[(rn e1 : : :en)]] apos; st def= [[rn]] ([[e1]] st) : : :([[en]] st) A[[(: a)]] apos; st def= IF A[[a]] apos; st = T THEN F ELSE T A[[(a1 amp; a2)]] apos; st def= IF A[[a1]] apos; st = T THEN A[[a2]] apos; st ELSE F A[[(a1 _ a2)]] apos; st def= IF A[[a1]] apos; st = T THEN T ELSE A[[a2]] apos; st A[[(a1 ) a2)]] apos; st def= IF A[[a1]] apos; st = T THEN A[[a2]] apos; st ELSE T A[[ ]] apos; st def= apos; st A[[a [e1=x1; : : :; en=xn]]] apos; st def= A[[a]] apos; (st [x1 [[e1]] st] : : : [xn [[en]] st])... In PAGE 8: ... x | variable; e | expression; a | expression, Boolean expression or assertion; fn | n-ary function symbol; rn | n-ary relation symbol; s = [e1=x1; : : :; en=xn] | I-substitution. SUBvar: x [e1=x1; : : :; en=xn] = = nei if x = xi x if x 62 fx1; : : :; xng SUB var: [a= ] = a SUBfun: (fn e1 : : :en)s = = (fn e1s : : :ens) SUBrel: (rn e1 : : :en)s = = (rn e1s : : :ens) SUB rel: (rn e1 : : :en) [a= ] = = (rn e1 : : :en) SUBneg: (:a)s = (:as) SUB neg: (:a) [a= ] = (:a [a= ]) SUBc: (a1 c a2)s = (a1s c a2s) SUB c: (a1 c a2) [a= ] = = (a1 [a= ] c a2 [a= ]) (where c is any of amp;, _, )) SUBsubst: (a [e1=x1; : : :; en=xn])s = = a [e1s=x1; : : :; ens=xn; s0] where s0 is obtained from s by removing all pairs e0 i=xi for i = 1; : : :; n and opening its en- closing brackets SUB subst: (a1s) [a= ] = (a1 [a= ])s SUBeqvar: a [: : :; x=x; : : :] = a [: : :; : : :] SUBempty: a [ ] = a SUBcongr: a1 = a2 a1s = a2s SUB congr: a1 = a2 a1 [a= ] = a2 [a= ] Lemma 1 System SUB0 is sound wrt the semantics from Table4 with the interpretation of relation = as the extensional equality: j= a1 = a2 i 8 apos;:Sta!fT;Fg 8st:Sta A[[a1]] apos; st = A[[a2]] apos; st Proof of Lemma 1: 1A radical remedy for this shortcoming would be to distinguish between expressions that may occur in programs and expressions that may only occur in assertions.... In PAGE 9: ...Table4 . We are only giving the derivation of two of them leaving the remaining ones to the reader.... In PAGE 11: ...(x = x) = (x = 0) ) ( [1=x]) [(x = x)= ] = ( [1=x]) [(x = 0)= ] reduces to x = 0 ) 1 = 0 to which any such state st, that [[x]] st = 0, is a counterexample. Theorem 2 System SUB1 is sound with respect to the semantics from Table4 with the interpretation of relation = as the extensional equality. Proof of Theorem 2: This is a direct corollary from Lemma 1 and Lemma 2.... ..."
Table 3 shows the experimental results of distributed search algorithms on problems of different orders (each column represents an order). ABT and IDIBT used the domain/degree variable ordering, which was tested best in preliminary experiments. In the larger portfolios we used domain/degree and additional other heuristics including maxDegree, minDomain, lex and random. In all portfolios Aggregation with the method maxUsed was applied. For each order (column) we show the median runtime (in seconds) to solve 20 different problems (once each) and the number of solved problems. When less than 10 instances could be solved within a timeout of two hours we naturally cannot provide meaningful median results. In the experiments with M-ABT we have also observed runs which were aborted because of memory problems in our simulator. For order 8 these were about one third of the unsolved problems, for order 9 this problem occurred in all unsuccessful tests. This memory problem arising from the nogood-storage of ABT was addressed in [BBMM05] and is not subject to this research.
2005
"... In PAGE 10: ... Table3 . Median performance and instances solved (out of 20) of quasigroup comple- tion problems with 42% pre-assigned values.... ..."
Cited by 2
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