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Abstract Using Temporal Logic to Specify Adaptive Program Semantics

Abstract Using Temporal Logic to Specify Adaptive Program Semantics
Abstract Using Temporal Logic to Specify Adaptive Program Semantics

Using Temporal Logic to Specify

Adaptive Program Semantics?

Ji Zhang and Betty H.C.Cheng

Software Engineering and Network Systems Laboratory

Department of Computer Science and Engineering

Michigan State University

East Lansing,Michigan48824

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1Introduction

Increasingly,computer software must adapt to changing conditions in both the supporting computing and communication infrastructure,as well as in the surrounding physical environment(1).However,adaptive programs may be prone to errant behavior due to its innate high complexity and possibly im-precise requirements.The bene?t expected from adaptations,such as higher reliability and higher availability,may be countered by the errors introduced during the adaptive software development process.Furthermore,the correct-ness of adaptation becomes most crucial when the adaptive software is applied in a safety-critical domain,such as the control software for medical devices, etc.Correctness of adaptive programs cannot be properly addressed without precisely specifying the requirements for the adaptive programs.This paper introduces speci?cations for three commonly used adaptation semantics and two composition techniques to be used for constructing the temporal logic speci?cation of an adaptive program.

Numerous techniques have been proposed to address the correctness of adap-tations.Kulkarni et al.(2)introduced a transitional-invariant lattice approach that uses theorem proving techniques to show that during and after an adapta-tion,the adaptive program is always in correct states with respect to satisfying the transitional-invariants.Some approaches(3;4;5)use formal languages to describe the structural changes of the software at the design and implemen-tation levels.Other approaches(6;7;8;9;10;11;12;13)design adaptation protocols or algorithms to be used during component replacement to achieve a rigorous and safe adaptation procedure.Di?erent approaches assume di?erent adaptation semantics.For example,Appavoo et al.(12)introduced a hot-swapping technique,which does not allow the old and the new components to execute simultaneously to achieve strict sequential semantics.In contrast,in order to improve performance,Chen et al.(11)proposed a graceful adaptation process,which allows the old component and the new component to overlap their executions.These di?erent semantics are implied by their designs and implementations.All of the above techniques focus on the design and the im-plementation of achieving adaptability,while the requirements for adaptation are largely assumed or ambiguous.

We believe that the semantics for adaptive software should be explicitly cap-tured at the requirements level(14).The correctness of adaptive software can then be evaluated with respect to its adaptation requirements speci?cation.

A recent survey(15)describes numerous research e?orts that have proposed ways to formally specify dynamically adaptive programs in the past several years.Graph-based approaches model the dynamic architectures of adaptive programs as graph transformations(16;17;18).Architecture Description Lan-guage(ADL)-based approaches model adaptive programs with connection and

reconnection of connectors(10;19;4).A few e?orts have formally speci?ed the behavioral changes of adaptive programs,including those that use process algebras to specify the behavior of adaptive programs(20;21;22).

This paper focuses on the speci?cation of requirements of adaptations with temporal logics.We model an adaptive program as the composition of a?nite number of steady-state programs(20)(brie?y programs)and the adaptations among these programs.We assume that the properties(e.g.liveness and safety properties)of each program have already been speci?ed with a Linear Tempo-ral Logic(LTL)formula(23).To specify an adaptation from one program to another,we introduce the Adapt operator-extended LTL(A-LTL),an extension to LTL.We introduce three basic adaptation semantics and use A-LTL to for-mally specify their semantics.We can compose the basic adaptation semantics with two types of compositional procedures:neighborhood compositions and sequential compositions to derive more complex adaptation semantics.We introduce adaptation semantics graphs,a graph-based representation of adap-tive programs semantics,which can be automatically processed to generate adaptive program speci?cations.

The formal speci?cations for adaptive programs precisely describe the objec-tives for adaptive programs,which facilitate rigorous software requirements speci?cations,and thus improves the assurance of the software.The adap-tation semantics graphs and the generated temporal logic speci?cations may serve as guidance for the adaptation developers to clarify the intent for the adaptive program.The temporal speci?cations also enable us to perform nu-merous automated analyses of the adaptive program,such as speci?cation consistency,model checking to verify the correctness of a program model, dynamic insertion of adaptation logic code into the program,etc.

We have successfully applied our speci?cation technique to a number of adap-tive mobile computing applications,including MetaSockets(24).The remain-der of this paper is organized as follows.In Section2,we introduce A-LTL and describe three basic adaptation semantics.Section3describes neighborhood speci?cation compositions,sequential speci?cation compositions,and adapta-tion semantics graphs.Section4illustrates our approach with a MetaSockets example.Section5concludes the paper and discusses future directions.

2Adaptation Semantics

In this paper,we take a most general view of programs,i.e.,all programs and adaptive programs are considered as state machines(FSAs).An adaptive program changes its behavior during its execution.The state space describing each di?erent kind of steady-state(20)behavior is a non-adaptive program,or

brie?y a program.The state space describing the change of behavior from one program(source program)to another(target program)is a simple adaptive program,which can be identi?ed by the source and target program pair.

2.1Adapt Operator-Extended LTL

LTL is a temporal extension to propositional logic(23).An LTL formula is constructed over a set of atomic propositions(Π),using?(not)and∧(and) boolean operators,and (next)and U(until)temporal connectives.Other operators are de?ned as abbreviations over the above basic operators,such as ∨(or),→(imply),?(co-imply),?(eventually),2(always),etc.

Existing LTL operators are insu?cient for specifying adaptation behavior. In LTL,the operator closest to capturing the meaning of adaptation is the until(U)operator.However,we cannot express an adaptation from the be-havior speci?ed withφto the behavior speci?ed withψusingφUψfor the following reason:If a state sequenceσ=s0,s1,···satis?esφUψ(denoted as σ|=φUψ),thenφshould hold for all su?xes ofσstarting from all the states before a certain state.However,adaptation semantics usually only requireφto hold for a given interval ofσ.Several temporal logics,including the choppy logic(25),the Propositional Interval Temporal Logic(PITL)(26;27;28;29;

30),and the Interval Temporal Logic(ITL)(31),are capable of expressing adaptation behavior.However,these logics are both too complex and incon-venient for our adaptation speci?cation purposes.By“inconvenient”we mean that they do not have direct notation support.

To specify adaptation behavior,we extend LTL with the adapt operator (“? ”).Informally,a program satis?es“φ? ψ”(φ,ψ,and?are three tem-poral logic formulae)means that the program initially satis?esφ.In a certain state A,it stops being constrained byφ,and in the next state B,it starts to satisfyψ.We formally de?ne A-LTL as follows:

?Ifφis an LTL formula,thenφis also an A-LTL formula.

?Ifφandψare both A-LTL formulae,thenξ=φ? ψis an A-LTL formula.?Ifφandψare both A-LTL formulae,then?φ,φ∧ψ,φ∨ψ,andφUψare all A-LTL formulae.

We de?ne the A-LTL semantics as follows.

?Ifσis an in?nite state sequence andφis an LTL formula,thenσsatis?es φin A-LTL if and only ifσsatis?esφin LTL.Formally,σ|=φi?σ|=φin LTL.

?Ifσis a?nite state sequence andφis an A-LTL formula,thenσ|=fφi?σ |=φ,whereσ is an in?nite state sequence constructed by repeating the

last state ofσ.

?σ|=φ? ψi?there exist a?nite state sequenceσ =(s0,s1,···s k)and an in?nite state sequenceσ =(s k+1,s k+2,···),such thatσ=σ σ ,σ |=fφ,σ |=ψ,and(s k,s k+1)|=f?,whereφ,ψ,and?are A-LTL formulae.

A sequence satisfyingφ? ψcan be considered the concatenation of two subsequences,where the?rst subsequence satis?esφ,the second subse-quence satis?esψ,and the two states connecting the two subsequences satisfy?.

?Other operators(→,∧,∨,U,?,etc)are de?ned similarly as those used in LTL.

The?notation of an adapt operator can be used for specifying additional safe conditions for the state in which the adaptation occurs and logical connections between the behavior before and after adaptation.Although in some cases, the extra constraints are not used(where we simply set?to be true),we ?nd this notation to be useful in many other cases.For example,we can use?=bu?er-empty to constrain that the bu?er must be empty when the adaptation occurs.

We separate the behavior of an adaptive program into adaptation invariants, the properties that hold continuously during an execution of the program,and adaptation variants,the properties that change during the program’s execu-tion.We?rst introduce three adaptation semantics speci?cations in A-LTL. These semantics are used to specify the adaptation variants of adaptations from a given source program to a given target program.Then we introduce composition techniques to handle general adaptive programs.Finally,we com-pose adaptation invariants with adaptation variants to specify the overall adaptive program behavior.

2.2Semantics for Adaptation Variants

Several questions have to be answered before designing an adaptive program: (1)What is the expected behavior after adaptation?(2)What constraints,if any,exist for adaptations to occur?(3)Are the source program behavior and the target program behavior allowed to overlap?(4)Should the source/target program behavior be restricted during an adaptation,and what are the re-strictions,if any?An adaptation speci?cation should precisely address all of the above questions.Based on results presented in the literature and our own experience,we summarize three commonly used semantics for adaptation.By formally specifying these semantics,we are able to precisely answer the above questions.

We assume the source program and the target program have both been spec-i?ed in LTL,named base speci?cations.We specify the adaptation from the source program to the target program with A-LTL by extending the base speci?cations of the source and the target programs.For some adaptations, the source/target program behavior may need to be constrained during the adaptation.These constraints,termed restriction conditions,are speci?ed in LTL.

We assume the adaptive program has moderate computational re?ection(32) capability,i.e.,it is conscious about its adaptation and the currently running steady-state program.This capability can be achieved by simply introducing ?ag propositions in the program to identify its current steady-state program or adaptation status.We assume that a decision-making procedure that trans-lates environment changes into speci?c adaptation requests is available.Our speci?cation technique describes the expected program behavior in response to these requests.We use an atomic proposition A REQ to indicate the receipt of an adaptation request to a target program from the decision making procedure. In this section,we summarize three commonly occurring basic adaptation se-mantics interpretations from the literature(12;11;6;8)speci?ed in terms of A-LTL.There are potentially many other possible adaptation semantics. In all three adaptation speci?cation semantics,we denote the source and the target program base speci?cations as S SPEC and T SPEC,respectively.If appli-cable,the restriction condition during adaptation is R COND.We assume the ?ag propositions are already parts of the speci?cations.We use the term safe states to indicate the states where all the obligations of the source program are ful?lled,thus making it safe to terminate the source behavior.

2.2.1One-point adaptation.

Under one-point adaptation semantics,after receiving an adaptation request

A REQ,the program adapts to the target program T SPEC at a certain point

during its execution.The prerequisite for one-point adaptation is that the source program S SPEC should always eventually reach a safe state during its execution.

(S SPEC∧?A REQ)? T SPEC(1) The formula states that the program initially satis?es S SPEC.After receiving an adaptation request,A REQ,it waits until the program reaches a safe state, i.e.,all obligations generated by S SPEC are satis?ed.Then the program stops being obligated to satisfy S SPEC and starts to satisfy T SPEC.This semantics is

visually presented in Figure1(a),where circles represent a sequence of states. Solid lines represent state intervals and the label of each solid line represents the property that is held by the interval.The arrow points to the states when an adaptation request is received.This semantics is straightforward and is ex-plicitly or implicitly applied by many approaches(e.g.,(12;11;6))to deal with simple cases that do not require restraining the source behavior or overlapping the source and the target behavior.

2.2.2Guided adaptation.

Under guided adaptation semantics(visually depicted in Figure1(b)),after receiving an adaptation request,the program?rst restrains its source pro-gram behavior by a restriction condition,R COND,and then adapts to the target program when it reaches a safe state.This semantics is suitable for adapta-tions whose source programs do not guarantee reaching a safe state within a given amount of time.The restriction condition should ensure that the source program will?nally reach a safe state.

S SPEC∧(?A REQ?1 R COND) ?2 T SPEC(2) This formula states that initially S SPEC is satis?ed.After an adaptation re-quest,A REQ,is received,the program should satisfy a restriction condition

R

COND(marked with?1 ).When the program reaches a safe state of the source,

the program stops being constrained by S SPEC,and starts to satisfy T SPEC (marked with?2 ).The hot-swapping technique introduced by Appavoo et al(12)and the safe adaptation protocol(6)introduced in our previous work use the guided adaptation semantics.

2.2.3Overlap adaptation.

Under overlap adaptation semantics(visually depicted in Figure1(c)),the tar-get program behavior starts before the source program behavior stops.During the overlap of the source and the target behavior,a restriction condition is applied to safeguard the correct behavior of the program.This adaptation se-mantics is appropriate for the case when continuous service from the adaptive program is required.The restriction condition should ensure that the source program reaches a safe state.

S SPEC∧(?A REQ?1 R COND) ?2 true ∧ ?A REQ?1 T SPEC∧(R COND?2 true) (3)

This formula states that initially S SPEC is satis?ed.After an adaptation re-

quest,A REQ ,is received,the program should start to satisfy T SPEC and also

satisfy a restriction condition,R COND (marked with ?1 ).When the program

reaches a safe state of the source program,the program stops being obliged by S SPEC and R COND (marked with ?2 ).The graceful adaptation protocol in-

troduced by Chen et al (11)and the distributed reset protocol introduced by Kulkarni et al (8)use the overlap adaptation

semantics.

SPEC S SPEC

T

SPEC S SPEC

T REQ

R

SPEC S SPEC

T

REQ

COND

Fig.1.Adaptation semantics

2.2.4Safety and Liveness Properties.

Temporal logics are often applied to the speci?cations of safety and liveness properties of a program.A safety property asserts something bad never hap-pens,while a liveness property asserts something good will eventually hap-pen (33).Although general forms of safety and liveness properties are not preserved by the adaptation semantics de?ned above,some common forms of safety and liveness properties are preserved.

We de?ne a formula to be a point safety property if and only if =2?η(read as ηnever holds during execution),where ηis a point formula (a formula that does not contain temporal operators).We de?ne a formula to be point

liveness property if and only if =2(α→?β)(read as it is always the case that ifαholds at some point,thenβwill eventually hold at a point after that point),where bothαandβare point formulae.

LEMMA1:

All three adaptation semantics preserve point safety properties.That is,if (S SPEC∨T SPEC)→2?η,whereηis a point property,thenξ→2?η,whereξis the adaptation speci?cation based on either semantics.We only provide the proof for the one-point adaptation case.Other cases can be proved similarly.

Proof

Let the adaptation speci?cationξbe(Formula1)

ξ=(S

∧?A REQ)? T SPEC

SPEC

For an arbitrary sequenceσ|=ξ,?σ |=S SPEC∧?A REQ andσ |=T SPEC, andσ=σ σ .Since(S SPEC∨T SPEC)→2?η,σ |=2?η,andσ |=2?η. Therefore,σ|=2?η.

This lemma implies that if a propositional invariant(such as a variable is never greater than a given value)should be held in both the source and the target subprograms,then the invariant is also held by a simple adaptive program under all three adaptation semantics.This conclusion does not apply to general temporal properties.

LEMMA2:

Point liveness properties are preserved by all three adaptation semantics. That is,if(S SPEC∨T SPEC)→2(α→?β),thenξ→2(α→?β),whereξis the adaptation speci?cation based on either semantics.Also,we only provide the proof for the one-point adaptation case.Other cases can be proved similarly.

Proof:

Let the adaptation speci?cationξbe(Formula1)

∧?A REQ)? T SPEC

ξ=(S

SPEC

For an arbitrary sequence s0,s1,···|=ξ,?i,such that s0,s1,···s i|=S SPEC∧?A REQ and s i+1,s i+2,···|=T SPEC.

Since(S SPEC∨T SPEC)→2(α→?β),we have s0,s1,···s i|=2(α→?β),and s i+1,s i+2,···|=2(α→?β).

For an arbitrary state s j,if s j|=α,then we have

?if j≤i,then there exists k(j

?if j>i,then there exists k(j

That is,s0,s1,···|=2(α→?β)

Therefore,we haveξ→2(α→?β).

3Speci?cation Compositions

Thus far,we have described how to specify simple adaptive programs.These speci?cations can be composed to describe multiple adaptation options from a single steady-state program.We can also link a sequence of adaptation spec-i?cations together to describe the behavior of executions with multiple adap-tation occurrences.

3.1Neighborhood Composition

A simple adaptive program starts from one program and may adapt to only one target program.A more complex adaptive program may adapt to di?erent target programs in response to di?erent adaptation requests.The neighborhood composition is proposed to specify multiple adaptation options from a single program.We de?ne the neighborhood adaptive program of a program S to be the composition of all the simple adaptive programs that share the same source program S.An execution starting from S can either adapt to a target program if a corresponding adaptation request is received,or remain in S if no adaptation request is received.Assume for all target programs,the properties of the simple adaptive program from S to the i th target program T i is speci?ed with an A-LTL formula STi SPEC,and the properties of S are speci?ed with

S SPEC.We can construct the speci?cation for the neighborhood of S by the

disjunction of S SPEC and STi SPEC.Let N SPEC be the neighborhood speci?cation of S,we have

N SPEC=k

i=1

STi

SPEC∨

S SPEC(4)

where k is the number of simple adaptive programs sharing the same source program S.

3.2Sequential Composition

A complex adaptive program may sequentially perform adaptations more than once during a single execution.For example,an adaptive program may start

from a program A,then sequentially perform adaptations from A to B to obtain program B,and B to C to obtain program C.Assume the properties of

A,B,and C are speci?ed with A

SPEC,B SPEC,and C SPEC,respectively.The A to

B adaptation speci?cation under a given adaptation semantics is a function of

A

SPEC and B SPEC:AB SPEC=ADAPT1(A SPEC,B SPEC).The B to C adaptation

speci?cation under a given adaptation semantics is a function of B SPEC and

C SPEC:BC SPEC=ADAPT2(B SPEC,C SPEC).The speci?cation of A to B to C

may be constructed by substituting AB SPEC for the B SPEC in BC SPEC.

ABC

SPEC

=ADAPT2(ADAPT1(A SPEC,B SPEC),C SPEC)(5)

Similarly,a?nite number of simple adaptive program speci?cations can be sequentially composed to construct the speci?cation of more complex adaptive programs.

THEOREM:

Both point safety and point liveness properties are preserved by the adap-tation semantics and the two types of compositions.

Proof outline:

This theorem can be proved by applying Lemma1and Lemma2.?For neighborhood compositions,from the lemmas,we know that if all base speci?cations imply a point safety(liveness)propertyφ,then all the disjuncts implyφas well.Then the disjunction(the neighborhood composition)also impliesφ.

?For sequential composition,we can prove the conclusion inductively.

(1)The base case states0-step sequential composition preserves point

safety and liveness properties.This has been proved by Lemma1

and Lemma2.

(2)Then we assume any n-step sequential composition preserves point

safety and liveness properties.

An n+1-step sequential composition can be considered as an n-step

sequential composition composed with a base speci?cation.Then we

can apply Lemma1and Lemma2to the n-step adaptation compo-

sition case and claim that all n+1-step sequential compositions also

preserve point safety and liveness properties.

3.3Adaptation Invariants

In spite of adaptations of a program,some properties should be held true throughout its execution.This type of properties,such as safety and liveness properties,are adaptation invariants.We use LTL formulae to specify adap-tation invariants of the program to ensure the properties that should be held throughout the execution of an adaptive program.

3.4Adaptation Speci?cation Visualization and Automatic Generation

An adaptive program can be visually represented as a graph,where programs are vertices and adaptations are arcs.We add temporal logic information to the graph so that we are able to derive the adaptation temporal logic speci?cation automatically from the graph.

We de?ne an adaptation semantics graph to be a tuple

(S,S0,A,P,Φ,Ψ,INV),where S is a set of vertices representing the set of programs in an adaptive program.S0?S is a set of initial vertices,repre-senting the initial programs of the adaptive program.FunctionΦ:S→LTL maps each program to the base speci?cation for the program.A?S×S is a set of arcs representing adaptations.Function P:A→semantics name maps an adaptation to one of the semantics names{one-point,guided,overlap}.Ψ:A→LTL is a partial function that maps guided or overlap adaptation to their associated restriction conditions.INV is the set of adaptation invariants. The adaptation speci?cations can be derived from the adaptation semantics graph.We have implemented a prototype,ASpecGen,which uses simple edge traversal to automate this process.The complexity of the algorithm is linear to the number of adaptations.

4Case Study

In this section we use MetaSockets(24)as an illustrative example to demon-strate our adaptation speci?cation approach.MetaSockets are constructed from the regular Java Socket and MulticastSocket classes;however,their inter-nal structure and behavior can be modi?ed at run time in response to external conditions.MetaSocket behavior can be adapted through the insertion and removal of?lters that manipulate the data stream.For example,?lters can perform encryption,decryption,forward error correction,compression,and so forth.

We consider a sender’s MetaSocket with three di?erent?lters:a data com-pression?lter(COM),a DES64-bit encryption?lter(DES64),and a DES 128-bit encryption?lter(DES128).The available adaptations are data com-pression?lter insertion and removal,and DES?lter replacement.Note that to enforce security,the DES?lters can only be replaced by other DES?lters, but cannot be removed.These?lters can be combined in four di?erent con?g-urations:DES64,DES128,DES64with COM(DES64COM),and DES128with COM(DES128COM).We consider the MetaSocket under each con?guration a program.The adaptive program is initially running in the DES64program.

4.1MetaSocket Speci?cations

The speci?cations of the programs are described as follows:

?DES64program:

DES64

SPEC =(2DES64

FL

)∧2(DES64Input(x)→?DES64Output(x))

The DES64Input(DES64Output)are events indicating the input(output) of a packet to(from)the MetaSocket under DES64con?guration.1The ?ag proposition DES64

FL

indicates that the program is running under the DES64con?guration.The formula states that under this con?guration,for every input packet to be encoded by the DES64?lter,the MetaSocket should eventually output a DES64encoded packet.The following program aspects can be interpreted in a similar way.

?DES128program:

DES128

SPEC

=(2DES128FL)∧(2(DES128Input(x)→?DES128Output(x)))?DES64COM program:

DES64COM

SPEC

=

(2DES64COM

FL

)∧(2(DES64COMInput(x)→?DES64COMOutput(x)))

?DES128COM program:

=

DES128COM

SPEC

(2DES128COM FL)∧(2(DES128COMInput(x)→?DES128COMOutput(x)))

To determine the semantics of each adaptation,we consider the following factors:(1)The MetaSocket component is designed for the transmission of real-time video and audio data.Therefore,we should minimize data blocking.

(2)We should not allow both the source and the target programs to input simultaneously because that will cause ambiguity in the input.(3)We should not allow the target program to output data before any output is produced from the source program,otherwise,it will complicate the logic on the receiver. Based on the above considerations,it is appropriate to apply the overlap semantics with conditions prohibiting the types of overlap discussed above.

2.

The adaptation semantics graph is visually presented in Figure

Fig.2.Visual representation of MetaSocket adaptation semantics graph

We use the DES64to DES128adaptation as an example to demonstrate the simple adaptive program speci?cation construction.In order to use overlap semantics,we have to de?ne restriction conditions to prevent overlap of the source and target programs input and the overlap of the source and target programs output.Therefore,we de?ne the restriction conditions to be

R

(DES64-DES128)=2(?DES64Input(x)∧?DES128Output(x)).(6) COND

The intuition for this restriction condition is that it should not accept any more DES64inputs and will not produce any DES128outputs until all DES64out-puts have been produced.Given the source and target program speci?cations, the overlap semantics,and the restriction condition,we apply Formula3to

derive the following speci?cation:

DES64-DES128

SPEC =(7)

DES64SPEC∧ ?A REQ(DES128)true R COND(DES64-DES128) true true ∧ A REQ(DES128)true DES128SPEC∧(R COND(DES64-DES128)true true) Formula(7)states that after the program receives an adaptation request to the DES128program(A REQ(DES128)),it should adapt to the DES128program. Furthermore,the behavior of DES64and DES128may overlap,and during the overlapping period,the program should satisfy the restriction condition

R COND(DES64-DES128).In this example,the?notation of the adaptation

operators are not used.We simply assign true to?in the four adapt operator locations.With the same approach,we specify other simple adaptive programs in the adaptive program.Further,both the source and the target speci?cations are point liveness properties.We can unify them with the following formula by disregarding the type of inputs and outputs.

2(Input(x)→?Output(x))(8) According to Lemma2,we can conclude that the adaptation also satis?es the point liveness property.

In addition to the speci?cation for adaptation behavior,the program should also satisfy a set of adaptation invariants to maintain its integrity.?Security invariant

:During program execution,the sender MetaSocket should not cause any loss of packets,i.e.,all input packets should be output:

INV2=2(Input(x)→?Output(x))

Note,this invariant is already guaranteed by the point liveness preservation property of the adaptation semantics.(Formula8.)

?Precedence invariant

temporal logic(PITL)(26;27;28;29;30),and the interval temporal logic (ITL)(31),are also su?cient to specify adaptation behavior.However,they are generally too complex,and therefore su?er from non-elementary complexity in their decision procedure(25;26;27;28).We?nd the A-LTL to be simple and su?ciently e?ective to specify adaptation behavior,given that the logic was designed to support adaptation explicitly.

We have developed an A-LTL model checking algorithm under the assump-tion that the formulae on the two sides of an adapt operator are distinguished by a proposition.This assumption is ensured by the?ag propositions in our speci?cation technique.Our procedure is a variant of the automata-theoretic model checking algorithm introduced by Vardi et al(38;39;40)and Bow-man and Thompson(27;28).The model checking algorithm has complexity exponential to the length of the formula and linear to the size of the program. Note that LTL model checking is in the same complexity class,and A-LTL is a superset of LTL.Therefore,it is unlikely for us to?nd a polynomial model checking algorithm.We are currently investigating optimization techniques, such as on-the-?y model checking proposed by Gerth et al(38).

Our future work includes automatically generating program models and code based on a given adaptation semantics,as well as developing e?cient model checking tools to verify the correctness of adaptive programs.

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3.摘要:论文摘要的字数壹般为300字左右。摘要是对论文的内容不加注释和评论的简短陈述,是文章内容的高度概括。主要内容包括:该项研究工作的内容、目的及其重要性;所使用的实验方法;总结研究成果,突出作者的新见解;研究结论及其意义。摘要中不列举例证,不描述研究过程,不做自我评价。 论文摘要后另起壹行注明本文的关键词,关键词是供检索用的主题词条,应采用能够覆盖论文内容的通用专业术语,符合学科分类,壹般为3~5个,按照词条的外延层次从大到小排列。 4.目录(目录示例见附件3):独立成页,包括论文中的壹级、二级标题、后记、参考文献、和附录以及各项所于的页码。 5.正文:包括前言、论文主体和结论 前言:为正文第壹部分内容,简单介绍本项研究的背景和国内外研究成果、研究现状,明确研究目的、意义以及要解决的问题。 论文主体:是全文的核心部分,于正文中应将调查、研究中所得的材料和数据加工整理和分析研究,提出论点,突出创新。内容可根据学科特点和研究内容的性质而不同。壹般包括:理论分析、计算方法、实验装置和测试方法、对实验结果或调研结果的分析和讨论,本研究方法和已有研究方法的比较等方面。内容要求论点正确,推理严谨,数据可靠,文字精炼,条理分明,重点突出。 结论:为正文最后壹部分,是对主要成果的归纳和总结,要突出创新点,且以简练的文字对所做的主要工作进行评价。 6.后记:对整个毕业论文工作进行简单的回顾总结,对给予毕业论文工作提供帮助的组织或个人表示感谢。内容应尽量简单明了,壹般为200字左右。 7.参考文献:是论文不可或缺的组成部分。它既可反映毕业论文工作中取材广博程度,又可反映文稿的科学依据和作者尊重他人研究成果的严肃态度,仍能够向读者提供有关

配合前面的ntheorem宏包产生各种定理结构

%=== 配合前面的ntheorem宏包产生各种定理结构,重定义一些正文相关标题===% \theoremstyle{plain} \theoremheaderfont{\normalfont\rmfamily\CJKfamily{hei}} \theorembodyfont{\normalfont\rm\CJKfamily{song}} \theoremindent0em \theoremseparator{\hspace{1em}} \theoremnumbering{arabic} %\theoremsymbol{} %定理结束时自动添加的标志 \newtheorem{definition}{\hspace{2em}定义}[chapter] %\newtheorem{definition}{\hei 定义}[section] %!!!注意当section为中国数字时,[sction]不可用! \newtheorem{proposition}{\hspace{2em}命题}[chapter] \newtheorem{property}{\hspace{2em}性质}[chapter] \newtheorem{lemma}{\hspace{2em}引理}[chapter] %\newtheorem{lemma}[definition]{引理} \newtheorem{theorem}{\hspace{2em}定理}[chapter] \newtheorem{axiom}{\hspace{2em}公理}[chapter] \newtheorem{corollary}{\hspace{2em}推论}[chapter] \newtheorem{exercise}{\hspace{2em}习题}[chapter] \theoremsymbol{$\blacksquare$} \newtheorem{example}{\hspace{2em}例}[chapter] \theoremstyle{nonumberplain} \theoremheaderfont{\CJKfamily{hei}\rmfamily} \theorembodyfont{\normalfont \rm \CJKfamily{song}} \theoremindent0em \theoremseparator{\hspace{1em}} \theoremsymbol{$\blacksquare$} \newtheorem{proof}{\hspace{2em}证明} \usepackage{amsmath}%数学 \usepackage[amsmath,thmmarks,hyperref]{ntheorem} \theoremstyle{break} \newtheorem{example}{Example}[section]

论文写作格式规范与要求(完整资料).doc

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论文的写作格式及规范

论文的写作格式及规范

附件9: 科学技术论文的写作格式及规范 用非公知公用的缩写词、字符、代号,尽量不出现数学式和化学式。 2作者署名和工作单位标引和检索,根据国家有关标准、数据规范为了提高技师、高级技师论文的学术质量,实现论文写的科学化、程序化和规范化,以利于科技信息的传递和科技情报的作评定工作,特制定本技术论文的写作格式及规范。望各位学员在注重科学研究的同时,做好科技论文撰写规范化工作。 1 题名 题名应以简明、确切的词语反映文章中最重要的特定内容,要符合编制题录、索引和检索的有关原则,并有助于选定关键词。 中文题名一般不宜超过20 个字,必要时可加副题名。英文题名应与中文题名含义一致。 题名应避免使作者署名是文责自负和拥有著作权的标志。作者姓名署于题名下方,团体作者的执笔人也可标注于篇首页地脚或文末,简讯等短文的作者可标注于文末。 英文摘要中的中国人名和地名应采用《中国人名汉语拼音字母拼写法》的有关规定;人名姓前名后分写,姓、名的首字母大写,名字中间不加连字符;地名中的专名和通名分写,每分写部分的首字母大写。 作者应标明其工作单位全称、省及城市名、邮编( 如“齐齐哈尔电业局黑龙江省齐齐哈尔市(161000) ”),同时,在篇首页地脚标注第一作者的作者简介,内容包括姓名,姓别,出生年月,学位,职称,研究成果及方向。

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