Software process measuring model

U ovom radu opisan je Model mjerenja softverskog procesa (MMSP). MMSP je metoda procjene softverskih procesa, kvantitativnog mjerenja i unapređenja procesa za organizacije koje se bave razvojem softvera (SPO). Metoda je razvijena dijelom na temelju poboljšanja metoda CMM/CMMI, Bootstrap i SPICE, i na standardima ESA PSS05 i ISO 90003. U žarištu MMSP-a je proces razvoja softvera u softverskim poduzećima. Članak objašnjava glavni koncept dobavljanja podataka o softverskim inženjerskim organizacijama i njihovim projektima pomoću temeljito izgrađenog upitnika. MMSP se može interpretirati kao metoda za opisivanje kakav je položaj organizacije i koje se promjene predlažu u slijedećim koracima. Osnovna ideja MMSP-a je utvrditi profil zrelosti procesa SPO-a. Ciljevi MMSP procjene su: a) izmjeriti i razviti profil zrelosti kvalitete procesa prikazom jakih i slabih strana procijenjenog SPŠO-a, b) derivirati korake za unapređenja iz prikazanog profila kvalitete procesa. Prikazan je rezultat procjene obavljene u jedan dan u organizaciji koja se bavi proizvodnjom softvera (SPO X) i Projekta X unutar SPO-a X koji je održan početkom listopada 2010. Rezultati procjene prikazuju ukupne organizacijske i metodološke razine za Projekt X. Organizacija je na razini zrelosti od 2,83. Metodologija je na razini zrelosti od 2,48. Ukupna razina zrelosti za organizaciju SPO X je na razini zrelosti od 2,42, dok je metodologija na razini zrelosti od 2,57. Organizacija članka je sljedeća: nakon uvoda u poglavlju jedan, poglavlje dva objašnjava razloge razvoja sustava MMSP. Poglavlje tri opisuje razvoj MMSP-a. Algoritam razina zrelosti je prikazan u slijedećem poglavlju. Poglavlje pet objašnjava evaluaciju SPO-a, rezultati procjene prikazani su u poglavlju šest. Poglavlje sedam sadrži zaključak, popis literature je u poglavlju osam.In this paper the Software Process Measuring Model (SPMM) is described. SPMM is a method for software process assessment, quantitative measurement and improvement for software producing organizations (SPOs). It has been developed partly based on a renovation of the CMM/CMMI, Bootstrap and SPICE methods, standards ESA PSS 05, and ISO 90003. SPMM focuses on the software development process in software production enterprises. The article explains the central concept of gaining data about software engineering organizations with a thoroughly constructed questionnaire. It gives a ground to measure the quality maturity level of organization and its projects. The SPMM can be interpreted as a method for describing where an organization stands and what changes are to be recommended in the next steps. The main idea of the SPMM is to determine the process maturity profile of an SPO. The goals of a SPMM self-assessment are: a) to measure and develop an SPO maturity quality profile showing strengths and weaknesses of the SPO assessed, b) to derive the steps for improvement from the shown quality profile. The result of one day assessment in software production organization X (SPO X), and Project X within the SPO X which was held at the beginning of October 2010 is presented. The result of the assessment showed the total organization and methodology maturity levels of the Project X. The organization is on maturity level 2,83. The methodology is on maturity level of 2,48. The total maturity level of the organization of SPO X is on maturity level of 2,42, and the methodology is on maturity level of 2,57. The organization of the paper is as follows: after the introduction in section one, section two explains the reasons of the SPMM development. Section three depicts the SPMM development. The maturity level algorithm is explicated in the next section. Section five explains the evaluation of the SPO, the assessment results are in section six. The conclusion is given in section seven, and the list of literature in section eight

Heptathlon evaluation model using Grey system theory

U ovom radu istražujemo djelotvornost teorije Sivih sustava za utvrđivanje redoslijeda najboljih atletičarki u sedmoboju. Postojeća metoda bodovanja u ženskom sedmoboju treba alternativni način bodovanja jer pokazuje neprihvatljivu pristranost prema nekim atletskim disciplinama dok druge zanemaruje. Izraz Sivi znači nešto siromašno, nepotpuno i neizvjesno, i posebno se odnosi na informaciju o sustavu. Sivi relacijski stupanj dobiven Sivom teorijom koristi se za uspostavu cjelokupnog i točnog modela za utvrđivanje redoslijeda sedmobojki. Predložena metoda bodovanja je točna i pokazano poboljšava pravednost i ispravnost rezultata ženskog sedmoboja.In this paper we investigate the effectiveness of the Grey system theory to determine the ranking of the best women athletes in heptathlon. The scoring method currently used in women\u27s heptathlon needs alternative scoring as it displays unacceptable bias towards some athletic disciplines while deferring others. The term Grey stands for poor, incomplete and uncertain, and is especially related to the information about the system. The Grey relational grade deduced by the Grey theory is used to establish a complete and accurate model for determining the ranking of the heptathletes. The proposed scoring method is accurate and is shown to improve fairness and results\u27 validity in women\u27s heptathlon

A model of road traffic as a resource risk loss in the elderly population of Croatia

Background and Purpose: Use of the road traffic resource inevitably leads to significant human andmaterial losses. Thus, the standardized death rate among older people (65years) in the European Union was 19.8 in 2000 and 11.9 in 2011. A model is proposed of human loss through RTA to establish the main resource losses and major risk loss factors for victimization of the elderly population (65years), as compared to the risk prone young population (18–24 years). Materials andMethods:Data onRTA in Croatia are obtained fromthe official government bulletin for road safety of the Ministry of the Interior for the period 2000–2011. Minimum and maximum number of victims and mean expected loss are used for determination of environmental risks and risk proneness. Results and Conclusions: A comparative victimization analysis for Croatia for the period 2000–2011 shows significant losses for mild and severe injury to younger RT participants, increased mild injury to elderly drivers, and increased severe injury among elderly pedestrians.Risk ismostly expressed in fatal RTA for younger participantswith risk proneness of 32.4%. The most exposed RT users are: severely injured elderly drivers with unprofitable and profitable risks between 32.8 % and 50.9 %, fatally injured elderly drivers with unprofitable and profitable risks between 43.2 % and 66.1 %, and fatally injured elderly pedestrians with unprofitable and profitable risks between 93.9%and 86.3%. These facts demand preventive actions for these users on the side of car and road designers and traffic educators as well

Improved bisector clustering of uncertain data using SDSA method on parallel processors

Razvrstavanje podataka s nesigurnošću je vrlo istraživano područje. Ovaj rad posvećen je razvrstavanju objekata koji imaju nesigurnost 2D položaja uzrokovanog gibanjem objekata. Položaj pokretnog objekta izvještava se periodički, i stoga položaj objekta sadrži nesigurnost i opisan je funkcijom gustoće razdiobe (PDF). Podaci o takvim objektima i njihovim položajima čuvaju se u distribuiranim bazama podataka. Broj objekata s nesigurnošću može biti jako velik i dobivanje kvalitetnog rezultata u razumnom vremenu je zahtijevan zadatak. Najjednostavnija metoda za razvrstavanje je UK-means, u kojoj se računaju sve očekivane udaljenosti (ED) od objekata do središta grozdova. Stoga je UK-means nedjelotvorna metoda. Kako bi se izbjeglo računanje očekivanih udaljenosti predstavljene su brojne metode za odbacivanje. U radu je dan pregled postojećih metoda i predložena kombinacija dviju metoda. Prva metoda je nazvana podjela područja skupa podataka (SDSA) i kombinirana je s poboljšanom simetralnom metodom kako bi se skratilo vrijeme razvrstavanja podataka s nesigurnošću. Pomoću SDSA metode područje skupa podataka je podijeljeno na mala pravokutna područja i promatraju se samo objekti koji se nalaze u tom području. Koristeći mala pravokutna područja nudi se mogućnost za paralelno procesiranje, jer su područja međusobno neovisna i mogu se računati na različitim jezgrama procesora. Provedeni su pokusi kako bi se pokazala uspješnost nove kombinirane metode.Clustering uncertain objects is a well researched field. This paper is concerned with clustering uncertain objects with 2D location uncertainty due to object movements. Location of moving object is reported periodically, thus location is uncertain and described with probability density function (PDF). Data about moving objects and their locations are placed in distributed databases. Number of uncertain objects can be very large and obtaining quality result within reasonable time is a challenging task. Basic clustering method is UK-means, in which all expected distances (ED) from objects to clusters are calculated. Thus UK-means is inefficient. To avoid ED calculations various pruning methods are proposed. A survey of existing clustering methods is given in this paper and a combination of two methods is proposed. The first method, called Segmentation of Data Set Area is combined with Improved Bisector pruning to improve execution time of clustering uncertain data. In SDSA method, data set area is divided in many small segments, and only objects in that small segment are observed. Using segments there is a possibility for parallel computing, because segments are mutually independent, thus each segment can be computed on different core of parallel processor. Experiments were conducted to evaluate the effectiveness of the combined methods

Critical measures of success for a software project

Prikazani rad pokušava utvrditi skup mjerila performanci i faktora utjecaja za razvojne softverske projekte, tako da se mogu učiniti valjane usporedbe performanci između dovršenih projekata. Mjerila performanci identificirana u ovom radu su ključna mjerila koja su identificirana kao dio skupine kritičnih mjerila uspješnosti, s obzirom da ona adresiraju važne atribute razvojnog softverskog projekta. Za četrdeset malih i srednje velikih softverskih projekata izmjerena su mjerila performanci i faktori utjecaja.The work presented here attempts to identify a set of software project performance measures and influence factors used by software development projects so that a valid comparison of performance can be made between completed projects. The performance measures identified in this document are core measures that are identified as a part of the set of critical measures of success since they address important attributes of a software development project. For forty small and medium sized software projects the performance measures and the influence factors were measured

Improved bisector clustering of uncertain data using SDSA method on parallel processors

Razvrstavanje podataka s nesigurnošću je vrlo istraživano područje. Ovaj rad posvećen je razvrstavanju objekata koji imaju nesigurnost 2D položaja uzrokovanog gibanjem objekata. Položaj pokretnog objekta izvještava se periodički, i stoga položaj objekta sadrži nesigurnost i opisan je funkcijom gustoće razdiobe (PDF). Podaci o takvim objektima i njihovim položajima čuvaju se u distribuiranim bazama podataka. Broj objekata s nesigurnošću može biti jako velik i dobivanje kvalitetnog rezultata u razumnom vremenu je zahtijevan zadatak. Najjednostavnija metoda za razvrstavanje je UK-means, u kojoj se računaju sve očekivane udaljenosti (ED) od objekata do središta grozdova. Stoga je UK-means nedjelotvorna metoda. Kako bi se izbjeglo računanje očekivanih udaljenosti predstavljene su brojne metode za odbacivanje. U radu je dan pregled postojećih metoda i predložena kombinacija dviju metoda. Prva metoda je nazvana podjela područja skupa podataka (SDSA) i kombinirana je s poboljšanom simetralnom metodom kako bi se skratilo vrijeme razvrstavanja podataka s nesigurnošću. Pomoću SDSA metode područje skupa podataka je podijeljeno na mala pravokutna područja i promatraju se samo objekti koji se nalaze u tom području. Koristeći mala pravokutna područja nudi se mogućnost za paralelno procesiranje, jer su područja međusobno neovisna i mogu se računati na različitim jezgrama procesora. Provedeni su pokusi kako bi se pokazala uspješnost nove kombinirane metode.Clustering uncertain objects is a well researched field. This paper is concerned with clustering uncertain objects with 2D location uncertainty due to object movements. Location of moving object is reported periodically, thus location is uncertain and described with probability density function (PDF). Data about moving objects and their locations are placed in distributed databases. Number of uncertain objects can be very large and obtaining quality result within reasonable time is a challenging task. Basic clustering method is UK-means, in which all expected distances (ED) from objects to clusters are calculated. Thus UK-means is inefficient. To avoid ED calculations various pruning methods are proposed. A survey of existing clustering methods is given in this paper and a combination of two methods is proposed. The first method, called Segmentation of Data Set Area is combined with Improved Bisector pruning to improve execution time of clustering uncertain data. In SDSA method, data set area is divided in many small segments, and only objects in that small segment are observed. Using segments there is a possibility for parallel computing, because segments are mutually independent, thus each segment can be computed on different core of parallel processor. Experiments were conducted to evaluate the effectiveness of the combined methods

Critical measures of success for a software project

Prikazani rad pokušava utvrditi skup mjerila performanci i faktora utjecaja za razvojne softverske projekte, tako da se mogu učiniti valjane usporedbe performanci između dovršenih projekata. Mjerila performanci identificirana u ovom radu su ključna mjerila koja su identificirana kao dio skupine kritičnih mjerila uspješnosti, s obzirom da ona adresiraju važne atribute razvojnog softverskog projekta. Za četrdeset malih i srednje velikih softverskih projekata izmjerena su mjerila performanci i faktori utjecaja.The work presented here attempts to identify a set of software project performance measures and influence factors used by software development projects so that a valid comparison of performance can be made between completed projects. The performance measures identified in this document are core measures that are identified as a part of the set of critical measures of success since they address important attributes of a software development project. For forty small and medium sized software projects the performance measures and the influence factors were measured

A model of road traffic as a resource risk loss in the elderly population of Croatia

Background and Purpose: Use of the road traffic resource inevitably leads to significant human andmaterial losses. Thus, the standardized death rate among older people (65years) in the European Union was 19.8 in 2000 and 11.9 in 2011. A model is proposed of human loss through RTA to establish the main resource losses and major risk loss factors for victimization of the elderly population (65years), as compared to the risk prone young population (18–24 years). Materials andMethods:Data onRTA in Croatia are obtained fromthe official government bulletin for road safety of the Ministry of the Interior for the period 2000–2011. Minimum and maximum number of victims and mean expected loss are used for determination of environmental risks and risk proneness. Results and Conclusions: A comparative victimization analysis for Croatia for the period 2000–2011 shows significant losses for mild and severe injury to younger RT participants, increased mild injury to elderly drivers, and increased severe injury among elderly pedestrians.Risk ismostly expressed in fatal RTA for younger participantswith risk proneness of 32.4%. The most exposed RT users are: severely injured elderly drivers with unprofitable and profitable risks between 32.8 % and 50.9 %, fatally injured elderly drivers with unprofitable and profitable risks between 43.2 % and 66.1 %, and fatally injured elderly pedestrians with unprofitable and profitable risks between 93.9%and 86.3%. These facts demand preventive actions for these users on the side of car and road designers and traffic educators as well

Software process measuring model

U ovom radu opisan je Model mjerenja softverskog procesa (MMSP). MMSP je metoda procjene softverskih procesa, kvantitativnog mjerenja i unapređenja procesa za organizacije koje se bave razvojem softvera (SPO). Metoda je razvijena dijelom na temelju poboljšanja metoda CMM/CMMI, Bootstrap i SPICE, i na standardima ESA PSS05 i ISO 90003. U žarištu MMSP-a je proces razvoja softvera u softverskim poduzećima. Članak objašnjava glavni koncept dobavljanja podataka o softverskim inženjerskim organizacijama i njihovim projektima pomoću temeljito izgrađenog upitnika. MMSP se može interpretirati kao metoda za opisivanje kakav je položaj organizacije i koje se promjene predlažu u slijedećim koracima. Osnovna ideja MMSP-a je utvrditi profil zrelosti procesa SPO-a. Ciljevi MMSP procjene su: a) izmjeriti i razviti profil zrelosti kvalitete procesa prikazom jakih i slabih strana procijenjenog SPŠO-a, b) derivirati korake za unapređenja iz prikazanog profila kvalitete procesa. Prikazan je rezultat procjene obavljene u jedan dan u organizaciji koja se bavi proizvodnjom softvera (SPO X) i Projekta X unutar SPO-a X koji je održan početkom listopada 2010. Rezultati procjene prikazuju ukupne organizacijske i metodološke razine za Projekt X. Organizacija je na razini zrelosti od 2,83. Metodologija je na razini zrelosti od 2,48. Ukupna razina zrelosti za organizaciju SPO X je na razini zrelosti od 2,42, dok je metodologija na razini zrelosti od 2,57. Organizacija članka je sljedeća: nakon uvoda u poglavlju jedan, poglavlje dva objašnjava razloge razvoja sustava MMSP. Poglavlje tri opisuje razvoj MMSP-a. Algoritam razina zrelosti je prikazan u slijedećem poglavlju. Poglavlje pet objašnjava evaluaciju SPO-a, rezultati procjene prikazani su u poglavlju šest. Poglavlje sedam sadrži zaključak, popis literature je u poglavlju osam.In this paper the Software Process Measuring Model (SPMM) is described. SPMM is a method for software process assessment, quantitative measurement and improvement for software producing organizations (SPOs). It has been developed partly based on a renovation of the CMM/CMMI, Bootstrap and SPICE methods, standards ESA PSS 05, and ISO 90003. SPMM focuses on the software development process in software production enterprises. The article explains the central concept of gaining data about software engineering organizations with a thoroughly constructed questionnaire. It gives a ground to measure the quality maturity level of organization and its projects. The SPMM can be interpreted as a method for describing where an organization stands and what changes are to be recommended in the next steps. The main idea of the SPMM is to determine the process maturity profile of an SPO. The goals of a SPMM self-assessment are: a) to measure and develop an SPO maturity quality profile showing strengths and weaknesses of the SPO assessed, b) to derive the steps for improvement from the shown quality profile. The result of one day assessment in software production organization X (SPO X), and Project X within the SPO X which was held at the beginning of October 2010 is presented. The result of the assessment showed the total organization and methodology maturity levels of the Project X. The organization is on maturity level 2,83. The methodology is on maturity level of 2,48. The total maturity level of the organization of SPO X is on maturity level of 2,42, and the methodology is on maturity level of 2,57. The organization of the paper is as follows: after the introduction in section one, section two explains the reasons of the SPMM development. Section three depicts the SPMM development. The maturity level algorithm is explicated in the next section. Section five explains the evaluation of the SPO, the assessment results are in section six. The conclusion is given in section seven, and the list of literature in section eight