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2010 | nr 26 Informatyka-społeczeństwo-zastosowania | 119-137
Tytuł artykułu

Techniki prognozowania nakładów projektowych i jakości oprogramowania w projektach IT

Treść / Zawartość
Warianty tytułu
Techniques for Predicting Development Effort and Software Quality in IT Projects
Języki publikacji
PL
Abstrakty
Prognozowanie nakładów projektowych i jakości oprogramowania jest wymagającym zadaniem w inżynierii oprogramowania zarówno dla naukowców, jak i praktyków. Wczesne modele parametryczne zostały zbudowane prawie czterdzieści lat temu, jednak nadal są używane w niektórych firmach IT oraz w celach naukowych. Obecnie zaobserwować możemy wzrost zainteresowania bardziej "inteligentnymi" technikami z zakresu sztucznej inteligencji lub zbliżonych. W większości analiz autorzy informują, że takie nowoczesne techniki są lepsze od modeli parametrycznych pod względem dokładności prognoz. Z praktycznego jednak punktu widzenia ważne jest podkreślenie, że żadna z tych technik nie jest idealna.(fragment tekstu)
EN
The most important dimensions in software project estimation are: development effort and software quality. Several predictive models have been proposed for these dimensions. Although some of these models provide useful input for decision makers, most of them are inherently limited for industrial use. The aims of this study are to: (1) compare existing applications of methods and (2) select a best technique for building intelligent and practical models for development effort and software quality prediction. In recent years various techniques were used, which are based on statistics, machine learning, artificial intelligence and similar. Authors who used a single technique often report a success their studies, only sometimes additionally noticing threats in repeatability of their predictions in other environments. Other authors compare the accuracy of predictions obtained using different techniques. The main problem in these analyses is the lack of straightforward confirmation of the usefulness of specific techniques in building predictive models. When one author finds that one technique performs the best in their study, another author obtains the best predictions using a different technique. Bayesian nets (BNs) appear to be the best suited approach to build predictive intelligent and practical model. This paper summarizes some applications of BNs in modelling different aspects of software engineering and discusses proposed Productivity Model for analysing trade-offs between effort, size and software quality.(original abstract)
Twórcy
  • Uniwersytet Szczeciński
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
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