ࡱ> #` 0bjbjmm sh$$$$H<<<<l=LH=h,@,@,@,@sA$AA qsss=``p$h^5$bEoAsAbEbE,@,@C.G.G.GbE,@$,@q.GbEq.G.GF$,@= 1<~ETU0yEy"y$AB.GCtCAAAGAAAbEbEbEbEHHH)HHH)HHH Usage of Weibull and other models for software faults prediction in AXE Lovre Hribar Ericsson Nikola Tesla, d.d.Croatia, Split E-mail:  HYPERLINK "mailto:Lovre.Hribar@ericsson.com" Lovre.Hribar@ericsson.com, Abstract: There are several families for software quality prediction techniques in development projects. All of them can be classified in several subfamilies. Each of these techniques has its own distinctive feature and it may not give correct prediction of quality for a scenario different from the one for which the technique was designed. All these techniques for software quality prediction are dispersed. One of them is Statistical and Probabilistic technique. The paper deals with software quality prediction techniques in development projects. Four different models based on statistical and probabilistic approach is presented and evaluated for prediction of software faults in very large development projects. 1. INTRODUCTION It is always about quality and cost. Or even better, it is always about ratio between quality and cost. Software quality prediction helps minimize software costs. The number of faults in a large software project has a significant impact on project performances and hence is an input to project planning [1], [2]. As the quality level of the final product is set at the beginning of the project, a large number of faults can result in project delays and cost overruns. It identifies risk prone software components and indicates software problems at earlier stages of development [3], [4]. This risk can be mitigated at initial stages and better effort estimates can be made [5]. At the same time this early handling of risks reduces the costs associated with the risks. This eventually will produce good quality software in terms of number of errors. In addition the software quality prediction has a significant role in easing the maintenance of software. In short, the prediction is helpful in software development, testing and maintenance activities. Predictions can either be made on the basis of historical data collected during implementation of same or similar projects [6], [7], or it can be made using the design metrics collected during design phase. Both are measurement-based prediction methods since they involve software metrics in the prediction process. Based on these metrics, it is possible to build prediction models which are either of statistical nature [8] or Artificial Intelligence (AI) based models [7], [9], [10], and [11]. This prediction will be of empirical nature and a given statistical model will do the job. With the help of such a prediction an organization can at least roughly predict quality of the next iteration. Based on these predictions, new goals can be set and better quality may be achieved. Initial work on software quality prediction was of statistical nature for example [12], [13]. The basic aim of software quality prediction is to facilitate development of better quality software [16], [17]. Better quality results in satisfied customer and healthier return on investment. Various organizations and public departments have been involved in studies related to quality prediction for example Commission of the European Communities Strategic program for Research in Information Technology [14], Swedish National Board for Industrial and Technical Development [4], Northern Telecom Limited, USA [5], Nortel, USA [15], National Sciences and Engineering Research Council of Canada [18], [19], National Science Foundation, USA [20], [21], NASA [3], and National Natural Science Foundation of China [9]. Importance of quality prediction cannot be denied in current era of information technology [21]. Instead, in these days organizations are becoming more quality conscious and customer oriented. This paper explore the depth and breadth of software quality prediction techniques based on statistical and probabilistic approach and identify future research towards a method to select a most suitable prediction technique for projects developed for AXE based on the four different models discussed in following sections. 2. WEIBULL DISTRIBUTION MODEL The Weibull distribution is by far the worlds most popular statistical model for life data. It is also used in many other applications, such as weather forecasting and fitting data of all kinds. Among all statistical techniques it may be employed for engineering analysis with smaller sample sizes than any other method. The Weibull Distribution was first published in 1939, over 60 years ago and has proven to be invaluable for life data analysis in aerospace, automotive, electric power, nuclear power, medical, dental, electronics, and every industry. The Weibull distribution is one of the most widely used lifetime distributions in reliability engineering.  INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/ch6_eqna_lg.gif" \* MERGEFORMATINET  Where, INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/ch6weibu__4.gif" \* MERGEFORMATINET   = scale parameter,  = shape parameter (or slope) and  = location parameter. It is a versatile distribution that can take on the characteristics of other types of distributions, based on the value of the shape parameter, . 2.1 The Effect of  on the Weibull Failure Rate Function The value of  has a marked effect on the failure rate of the Weibull distribution and inferences can be drawn about a population's failure characteristics just by considering whether the value of  is less than, equal to, or greater than one. Populations with  < 1 exhibit a failure rate that decreases with time, populations with  = 1 have a constant failure rate and populations with  > 1 have a failure rate that increases with time. All three life stages of the bathtub curve can be modeled with the Weibull distribution and varying values of . The Weibull failure rate for 0 <  < 1 is unbounded at T = 0 (or ). The failure rate, (T), decreases thereafter monotonically and is convex, approaching the value of zero. This behavior makes it suitable for representing the failure rate of units exhibiting early-type failures, for which the failure rate decreases with age. When encountering such behavior in a manufactured product, it may be indicative of problems in the production process, inadequate burn-in, substandard parts and components, or problems with packaging and shipping. For  = 1, (T) yields a constant value and this makes it suitable for representing the failure rate of chance-type failures and the useful life period failure rate of units. For  > 1, (T) increases as T increases and becomes suitable for representing the failure rate of units exhibiting wear-out type failures. For 1 <  < 2, the (T) curve is concave, consequently the failure rate increases at a decreasing rate as T increases. For  = 2 there emerges a straight line relationship between (T) and T, starting at a value of (T) = 0 at T = , and increasing thereafter. Note that at  = 2, the Weibull distribution equations reduce to that of the Rayleigh distribution. When  > 2, the (T) curve is convex, with its slope increasing with T. Consequently, the failure rate increases at an increasing rate as T increases indicating wear-out life. 2.2 The Effect of the Scale Parameter, , for the Weibull distribution A change in the scale parameter  has the same effect on the distribution as a change of the abscissa scale. Increasing the value of  while holding  constant has the effect of stretching out the pdf. Since the area under a pdf curve is a constant value of one, the "peak" of the pdf curve will also decrease with the increase of . If  is increased while  and  are kept the same, the distribution gets stretched out to the right and its height decreases, while maintaining its shape and location. If  is decreased while  and  are kept the same, the distribution gets pushed in towards the left (i.e. towards its beginning or towards 0 or ), and its height increases ( has the same units as T, such as hours, miles, cycles, actuations, etc.) 2.3 The Effect of the Location Parameter, , for the Weibull distribution The location parameter, , as the name implies, locates the distribution along the abscissa. Changing the value of  has the effect of "sliding" the distribution and its associated function either to the right (if  > 0) or to the left (if  < 0). When  = 0, the distribution starts at T = 0 or at the origin. If  > 0, the distribution starts at the location  to the right of the origin. If  < 0, the distribution starts at the location  to the left of the origin ( provides an estimate of the earliest time-to-failure of such units.) The life period 0 to + are a failure free operating period of such units. The parameter  may assume all values and provides an estimate of the earliest time a failure may be observed. A negative  may indicate that failures have occurred prior to the beginning of the test, namely during production, in storage, in transit, during checkout prior to the start of a mission, or prior to actual use ( has the same units as T, such as hours, miles, cycles, actuations, etc.) 3. LOGNORMAL DISTRIBUTION MODEL The lognormal distribution is commonly used to model the lives of units whose failure modes are of a fatigue-stress nature.  INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/ch9_eqnb_lg.gif" \* MERGEFORMATINET  Where:  INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/ch9logno__13.gif" \* MERGEFORMATINET  Since this includes most, if not all, mechanical systems, the lognormal distribution can have widespread application. Consequently, the lognormal distribution is a good companion to the  HYPERLINK "http://www.weibull.com/LifeDataWeb/the_weibull_distribution.htm" Weibull distribution when attempting to model these types of units. 3.1 Characteristics of the lognormal distribution As may be surmised by the name, the lognormal distribution has certain similarities to the  HYPERLINK "http://www.weibull.com/LifeDataWeb/the_normal_distribution.htm" normal distribution. A random variable is lognormal distributed if the logarithm of the random variable is normally distributed. Because of this, there are many mathematical similarities between the two distributions. The lognormal distribution is a distribution skewed to the right. The pdf starts at zero, increases to its mode, and decreases thereafter. The degree of skew ness increases as increases, for a given INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/mu_prime.gif" \* MERGEFORMATINET . For the same INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/sigma_t_prime.gif" \* MERGEFORMATINET , the pdf's skew ness increases as  INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/mu_prime.gif" \* MERGEFORMATINET increases. For  INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/sigma_t_prime.gif" \* MERGEFORMATINET values significantly greater than 1, the pdf rises very sharply in the beginning, i.e. for very small values of T near zero, and essentially follows the ordinate axis, peaks out early, and then decreases sharply like an exponential pdf or a Weibull pdf with 0 <  < 1. The parameter, INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/mu_prime.gif" \* MERGEFORMATINET  in terms of the logarithm of the T's is also the scale parameter, and not the location parameter as in the case of the normal pdf. The parameter INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/sigma_t_prime.gif" \* MERGEFORMATINET , or the standard deviation of the T's in terms of their logarithm or of their INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/t_prime.gif" \* MERGEFORMATINET , is also the shape parameter and not the scale parameter, as in the normal pdf, and assumes only positive values. 4. NORMAL DISTRIBUTION MODEL The normal distribution, also known as the Gaussian distribution, is the most widely-used general purpose distribution. It is for this reason that it is included among the lifetime distributions commonly used for reliability and life data analysis. There are some who argue that the normal distribution is inappropriate for modeling lifetime data because the left-hand limit of the distribution extends to negative infinity. This could conceivably result in modeling negative times-to-failure. However, provided that the distribution in question has a relatively high mean and a relatively small standard deviation, the issue of negative failure times should not present itself as a problem. Nevertheless, the normal distribution has been shown to be useful for modeling the lifetimes of consumable items. The pdf of the  HYPERLINK "http://www.weibull.com/LifeDataWeb/the_normal_distribution.htm" normal distribution is given by:  INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/ch8_eqna_lg.gif" \* MERGEFORMATINET   INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/ch8norma__3.gif" \* MERGEFORMATINET  Where: INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/ch8norma__4.gif" \* MERGEFORMATINET  It is a two-parameter distribution with parameters  (or INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/t_hat.gif" \* MERGEFORMATINET ) and T, i.e. the mean and the standard deviation, respectively. 4.1 Characteristics of the Normal distribution The normal pdf has no shape parameter. This means that the normal pdf has only one shape, the bell shape, and this shape does not change. The standard deviation, T, is the scale parameter of the normal pdf. As T decreases, the pdf gets pushed toward the mean, or it becomes narrower and taller. As T increases, the pdf spreads out away from the mean, or it becomes broader and shallower. The standard deviation can assume values of 0 < T < INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/infinity.gif" \* MERGEFORMATINET . The standard deviation is also the distance between the mean and the point of inflection of the pdf, on each side of the mean. The point of inflection is that point of the pdf where the slope changes its value from a decreasing to an increasing one, or where the second derivative of the pdf has a value of zero. The normal pdf starts at T = -  INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/infinity.gif" \* MERGEFORMATINET  with an f(T) = 0. As T increases, f(T) also increases, goes through its point of inflection and reaches its maximum value at T =  INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/t_hat.gif" \* MERGEFORMATINET . Thereafter, f(T) decreases, goes through its point of inflection, and assumes a value of f(T) = 0 at T = +  INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/infinity.gif" \* MERGEFORMATINET . 5. EXPONENTIAL DISTRIBUTION MODEL The exponential distribution is a commonly used distribution in  HYPERLINK "http://www.weibull.com/LifeDataWeb/reliability_engineering.htm" reliability engineering. Mathematically, it is a fairly simple distribution, which many times lead to its use in inappropriate situations. It is, in fact, a special case of the Weibull distribution where  - 1. 5.1 Characteristics of the Exponential distribution The exponential distribution is used to model the behavior of units that have a constant failure rate (or units that do not degrade with time or wear out). As mentioned before, the primary trait of the  HYPERLINK "http://www.weibull.com/LifeDataWeb/the_exponential_distribution.htm" exponential distribution is that it is used for modeling the behavior of items with a constant failure rate. It has a fairly simple mathematical form, which makes it fairly easy to manipulate. Unfortunately, this fact also leads to the use of this model in situations where it is not appropriate. However, some inexperienced practitioners of reliability engineering and life data analysis will overlook this fact, lured by the siren-call of the exponential distribution's relatively simple mathematical models. The exponential pdf has no shape parameter, as it has only one shape. The exponential pdf is always convex and is stretched to the right as  decreases in value. The value of the pdf function is always equal to the value of  at T = 0 (or T = ). The location parameter, , if positive, shifts the beginning of the distribution by a distance of  to the right of the origin, signifying that the chance failures start to occur only after  hours of operation, and cannot occur before this time. The scale parameter is INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/ch7expon__74.gif" \* MERGEFORMATINET . As INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/ch7expon__75.gif" \* MERGEFORMATINET , INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/ch7expon__76.gif" \* MERGEFORMATINET . The one-parameter exponential reliability function starts at the value of 100% at T = 0, decreases thereafter monotonically and is convex. The two-parameter exponential reliability function remains at the value of 100% for T = 0 up to T = , and decreases thereafter monotonically and is convex. As INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/ch7expon__83.gif" \* MERGEFORMATINET  INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/ch7expon__83.gif" \* MERGEFORMATINET , INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/ch7expon__84.gif" \* MERGEFORMATINET . The reliability for mission duration of INCLUDEPICTURE "http://www.weibull.com/LifeDataWeb/ch7expon__85.gif" \* MERGEFORMATINET , or of one MTTF duration, is always equal to 0.3679 or 36.79%. This means that the reliability for a mission which is as long as one MTTF is relatively low and is not recommended because only 36.8% of the missions will be completed successfully. In other words, of the equipment undertaking such a mission, only 36.8% will survive their mission. 6. MEASUREMENT RESULTS A fault occurrence as a customer reported problem that requires developer intervention to correct is defined. This is the observable event of interest for both maintenance and insurance purposes. The operational definition of a fault occurrence varies across organizations. In this paper, we use the approach to analyze fault occurrences in Ericsson Nikola Tesla R&D organizations using the term Trouble Reports (TR) and the TR inflow over the weeks. The TRs are reports either from the customer, either from the internal verification team, either from ongoing development projects. In general, not every reported TR reports the fault. After analysis some reports are classified as a non-fault but as a misconfiguration or misunderstanding the function. The software development process at ETK R&D organization constantly during all development stages in the project measure faults and failures. The goals is to find fault as much as possible earlier and to fixed them and to save hours and money for the organization internally and Ericsson in global. Our findings support the idea that a common fault occurrence projection method for ETK R&D can be used across many R&D organizations either inside the Ericsson Company, either outside Ericsson or for similar or even different development styles.  Weibull model  Lognormal model We are interested in the fault occurrence pattern, which is the rate of fault occurrence as a function of time over the lifetime of a product or several product lines. Software system functioning in IP network that businesses are increasingly dependent upon was examined. This system is multirelease, in customer operation over 3 years, single-platform, and widely-deployed. It is generally accepted that these widely-deployed, production, software systems are not fault free and that there is a need to manage the risks associated with the fault occurrences. The models are simulated in SCILAB [22] and the parameters for the different distributions are calculated in Microsoft Excel [23]. Empirically are addressed two questions that are important for fault occurrence projection: Is there a type of fault occurrence model that provides a good fit to fault occurrence patterns across multiple releases and in many organizations? Given such a model, how can model parameters for a new release be extrapolated using historical information?  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Data are used from a diverse sample of program releases developed with single development styles (commercial) inside the AXE platform. Data are gathered from 201 weeks in operation. The results show that the Weibull model is the preferred one among 4 different models due to the fact that the correlation coefficient was the highest value among all models. Also, exponential model is inadequate for prediction of software faults.  Exponential model 7. FUTURE WORK Proposed models should be developed further on to find out on the larger sample of the software units the best suitable models for the software faults prediction. Not only the software units should be evaluate, but also the applications in both mobile and fixed networks. Models should also evaluate and be combined with other quality methods taking the right preventive/corrective actions within the projects. 8. CONCLUSION The results presented in this paper can help in future planning the projects taking into the considerations the faults currently existing in the systems. Presented and evaluated models can help in prediction in number of the faults in the system during his lifetime and consequently help improve the quality of the product taking the right preventive actions. These data can be used for several purposes: in the project planning about people capacity in the design follow-up project, as a measure when the product reach his maturity level for design maintenance project or in design maintenance project for maintaining life. Especially, the Weibull model is the best one among four different evaluated models and the data collected for one product/program can help in fault prediction for another product or for faults prediction for the design projects in overall. 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All Rights Reserved. ; ;================================================================= ; ; Default Font Mapping Table for Director 7.0 Macintosh and Windows. ; ; This file provides a font mapping table for Director 7.0 for Windows ; and Macintosh. ; ; If a copy of this file is in the same folder or directory as the ; Director application, Director will automatically include this font ; mapping table in every new movie you create. ; ; To add this font mapping table to an existing movie, choose ; Movie:Properties... from the Modify menu. Then click Load from File. ; Use the dialog box that appears to locate this file. ; ; Note: In this file, a semicolon at the beginning of a line indicates ; a comment. ; ;================================================================= ; ; FONT MAPPINGS ; ; Font mappings specify which font and size substitutions to make when ; moving a movie from one platform to another. ; ; The format for font mapping definitions is: ; ; Platform:FontName => Platform:FontName [MAP NONE] [oldSize => newSize] ; ; Specifying MAP NONE turns off character mapping for this font. ; If you specify size mappings, they apply for THAT FONT ONLY. ; ; Here are some typical mappings for the standard Macintosh fonts: ; Mac:Chicago => Win:System Mac:Courier => Win:"Courier New" Mac:Geneva => Win:"MS Sans Serif" Mac:Helvetica => Win:Arial Mac:Monaco => Win:Terminal Mac:"New York" => Win:"MS Serif" Mac:Symbol => Win:Symbol Map None Mac:Times => Win:"Times New Roman" 14=>12 18=>14 24=>18 30=>24 Mac:Palatino => Win:"Times New Roman" ; ; Here are some typical mappings for the standard Windows fonts: ; Win:Arial => Mac:Helvetica Win:"Courier" => Mac:Courier Win:"Courier New" => Mac:Courier Win:"MS Serif" => Mac:"New York" Win:"MS Sans Serif" => Mac:Geneva Win:Symbol => Mac:Symbol Map None Win:System => Mac:Chicago Win:Terminal => Mac:Monaco Win:"Times New Roman" => Mac:"Times" 12=>14 14=>18 18=>24 24=>30 ; Note: When mapping from Windows to Macintosh, Courier and Courier New ; map onto Courier. When coming back to Windows only Courier New ; will be used. ; Japanese Font Mappings ; ; The Macintosh Japanese Osaka font is mapped to a Windows font, and ; all Windows fonts are mapped to Macintosh's Osaka. Map None is used ; because only Roman fonts need upper-ASCII characters mapped. To prevent ; mapping of any additional Japanese fonts, add them to this list. ; ; Note: If you do not have a Japanese system, the font names below ; will appear to be unreadable. Mac:Osaka=>Win:"lr SVbN" Map None Win:"lr SVbN"=>Mac:Osaka Map None Win:"lr "=>Mac:Osaka Map None Win:"WSVbN"=>Mac:Osaka Map None Win:"W"=>Mac:Osaka Map None Win:""=>Mac:Osaka Map None ;================================================================= ; ; CHARACTER MAPPINGS ; ; Character mapping ensures that characters such as bullets, ; quote marks, and accented characters always appear correctly ; when text is moved from one platform to another. When a ; character is mapped, a different ASCII value is substituted ; in order to preserve the appearance of the character. ; ; Character mappings are used for all fonts EXCEPT those declared ; above as Map None. ; ; The format for character mappings is: ; ; Platform: => Platform: oldChar => oldChar ... ; ; The following table provides a full set of bi-directional ; mappings for all ASCII values between 128 and 255. ; ; Note: Some characters are not available in both character sets. ; However, the bi-directional mapping table below preserves these ; characters even if they are mapped to a different platform and ; later re-mapped back to the original platform. Mac: => Win: 128=>196 129=>197 130=>199 131=>201 132=>209 133=>214 134=>220 Mac: => Win: 135=>225 136=>224 137=>226 138=>228 139=>227 140=>229 141=>231 Mac: => Win: 142=>233 143=>232 144=>234 145=>235 146=>237 147=>236 148=>238 Mac: => Win: 149=>239 150=>241 151=>243 152=>242 153=>244 154=>246 155=>245 Mac: => Win: 156=>250 157=>249 158=>251 159=>252 160=>134 161=>176 162=>162 Mac: => Win: 163=>163 164=>167 165=>149 166=>182 167=>223 168=>174 169=>169 Mac: => Win: 170=>153 171=>180 172=>168 173=>141 174=>198 175=>216 176=>144 Mac: => Win: 177=>177 178=>143 179=>142 180=>165 181=>181 182=>240 183=>221 Mac: => Win: 184=>222 185=>254 186=>138 187=>170 188=>186 189=>253 190=>230 Mac: => Win: 191=>248 192=>191 193=>161 194=>172 195=>175 196=>131 197=>188 Mac: => Win: 198=>208 199=>171 200=>187 201=>133 202=>160 203=>192 204=>195 Mac: => Win: 205=>213 206=>140 207=>156 208=>173 209=>151 210=>147 211=>148 Mac: => Win: 212=>145 213=>146 214=>247 215=>215 216=>255 217=>159 218=>158 Mac: => Win: 219=>164 220=>139 221=>155 222=>128 223=>129 224=>135 225=>183 Mac: => Win: 226=>130 227=>132 228=>137 229=>194 230=>202 231=>193 232=>203 Mac: => Win: 233=>200 234=>205 235=>206 236=>207 237=>204 238=>211 239=>212 Mac: => Win: 240=>157 241=>210 242=>218 243=>219 244=>217 245=>166 246=>136 Mac: => Win: 247=>152 248=>150 249=>154 250=>178 251=>190 252=>184 253=>189 Mac: => Win: 254=>179 255=>185 Win: => Mac: 128=>222 129=>223 130=>226 131=>196 132=>227 133=>201 134=>160 Win: => Mac: 135=>224 136=>246 137=>228 138=>186 139=>220 140=>206 141=>173 Win: => Mac: 142=>179 143=>178 144=>176 145=>212 146=>213 147=>210 148=>211 Win: => Mac: 149=>165 150=>248 151=>209 152=>247 153=>170 154=>249 155=>221 Win: => Mac: 156=>207 157=>240 158=>218 159=>217 160=>202 161=>193 162=>162 Win: => Mac: 163=>163 164=>219 165=>180 166=>245 167=>164 168=>172 169=>169 Win: => Mac: 170=>187 171=>199 172=>194 173=>208 174=>168 175=>195 176=>161 Win: => Mac: 177=>177 178=>250 179=>254 180=>171 181=>181 182=>166 183=>225 Win: => Mac: 184=>252 185=>255 186=>188 187=>200 188=>197 189=>253 190=>251 Win: => Mac: 191=>192 192=>203 193=>231 194=>229 195=>204 196=>128 197=>129 Win: => Mac: 198=>174 199=>130 200=>233 201=>131 202=>230 203=>232 204=>237 Win: => Mac: 205=>234 206=>235 207=>236 208=>198 209=>132 210=>241 211=>238 Win: => Mac: 212=>239 213=>205 214=>133 215=>215 216=>175 217=>244 218=>242 Win: => Mac: 219=>243 220=>134 221=>183 222=>184 223=>167 224=>136 225=>135 Win: => Mac: 226=>137 227=>139 228=>138 229=>140 230=>190 231=>141 232=>143 Win: => Mac: 233=>142 234=>144 235=>145 236=>147 237=>146 238=>148 239=>149 Win: => Mac: 240=>182 241=>150 242=>152 243=>151 244=>153 245=>155 246=>154 Win: => Mac: 247=>214 248=>191 249=>157 250=>156 251=>158 252=>159 253=>189 Win: => Mac: 254=>185 255=>216 lRTXI# INETURL.X32INetUrl PPC XtraI# NETFILE.X32NetFile PPC XtraK% Netlingo.x32NetLingo PPC XtraJ$mix services.x32 Mix ServicesX2Sound Import Export.x32Sound Import ExportT. swadcmpr.x32SWA Decompression PPC XtraO) SWASTRM.X32SWA Streaming PPC XtraF  TextXtra.x32 TextXtra PPCI#Text Asset.x32 TextAsset PPCH" Font Xtra.x32 Font Xtra PPCJ$Font Asset.x32Font Asset PPC2 MacroMix.x32L&Flash Asset.x32Flash Asset PPCI# QT3Asset.X32QuickTime Asset5DirectSound.x32LsCM:  InternalSREVULOCF8"""333DDDUUUfffwwwFRCSFRCSfniC6 _*SAC< &$tSAC4(supertSACM%p1?+2AtSACM%p2[35AtSACM%p3a5=AtSACM%p4]4pUvPf P)@*~Q9_6(-ZP*'/xK8tv E~Bj|mE6SK=)bqEL|>d!ZZ =OɎFD:"WVQlM-^Ҕ4}~abxo[I-[eaUEk>3,)ZnE\f>ipD[ݫVs$Ikp䖎ޫюHVe"r.[2BF> Op ",U]BLe"`0 CCh&9*N8_S[ FcۚT18E"@P9YGiTP@x$2'c2v{z~6] rю^s*K"C:Xx촴W_WԬODgpI8}pэNVz\MfUoSHE4ŗ%EHuL@ژj=͢sjB_5ډR,fJf"I)D(![TЭ'g3?C봵w~=<熹󙙽9W4s';{jsPĪr?Z[s!} ZKUn~\d/ SpPc(?O6ZJ\{{y\>eؕj[C D?wfzO p -z^T? ̻LW I}&c*?ԳDOA*a| 63xo\~w;I3n٧NٷS~/v6>Fc\[҆`ΉkfG~fKڎ8;, 3jXAŮ*Y xG|卮>Gg]Aj_u6s7ʪ{f}[etՐ+ #m6LD'*zfаc0%!%GNn7V$aQe"wj?. ~S/|7d!H, `nI60JeN1S Sc˵^ѦiyDzi*sLk5mnm ggh;@%:j 9<^^P$idVQaj10*)Lc #xʤ Gjw8-qK_ektkf}+3iH[ y)sBNesumvttmԺ!$P7EWľô@k6 ;ߠ/^_Y/K~,ty߷poa-(37'Ko7nyZhmڔ*@i.ǐb?$ ]3]חytmԺ/wf@P+44 *~Q9_6(-ZP*=~Qyl. 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