Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Software Quality Journal
Erschienen in: Software Quality Journal 2/2018

08.04.2017

Stability prediction of the software requirements specification

verfasst von: José del Sagrado, Isabel M. del Águila

Erschienen in: Software Quality Journal | Ausgabe 2/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Complex decision-making is a prominent aspect of Requirements Engineering. This work presents the Bayesian network Requisites that predicts whether the requirements specification documents have to be revised. We test Requisites’ suitability by means of metrics obtained from a large complex software project. Furthermore, this Bayesian network has been integrated into a software tool by defining a communication interface inside a multilayered architecture. In this way, we add a new decision-making functionality that provides requirements engineers with a feature to explore software requirement specification by combining requirement metrics and the probability values estimated by the Bayesian network.
Vorheriger Artikel An empirical study of crash-inducing commits in Mozilla Firefox
Nächster Artikel Towards improving decision making and estimating the value of decisions in value-based software engineering: the VALUE framework

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
Abran, A., Moore, J., Bourque, P., Dupuis, R., & Tripp, L. (2004). Guide to the Software Engineering Body of Knowledge. Los Alamitos: IEEE Computer Society.
del Águila, I. M., & del Sagrado, J. (2011). Requirement risk level forecast using Bayesian networks classifiers. International Journal of Software Engineering and Knowledge Engineering, 21(2), 167–190.CrossRef
del Águila, I. M., & del Sagrado, J. (2015). Bayesian networks for enhancement of requirements engineering, a literature review. Requirements Engineering, 1–20.
del Águila, I. M., del Sagrado, J., Túnez, S., & Orellana, F. J. (2010). Seamless software development for systems based on Bayesian networks - an agricultural pest control system example, 5th International Conference on Software and Data Technologies, (ICSOFT), (Vol. 2 pp. 456–461). Athens.
Alexander, I., & Beus-Dukic, L. (2009). How to specify products and services. Discovering requirements. New York: Wiley.
Bagnall, A. J., Rayward-Smith, V. J., & Whittley, I. (2001). The next release problem. Information & Software Technology, 43(14), 883–890.CrossRef
Cañadas, J., Orellana, F. J., del Águila, I., Palma, J., & Túnez, S. (2009). A tool suite for hybrid intelligence information systems, Proceedings of Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA’09) (pp. 9–13). Sevilla.
Castro-Herrera, C., Cleland-Huang, J., & Mobasher, B. (2009). Enhancing stakeholder profiles to improve recommendations in online requirements elicitation. atlanta, georgia, usa, 17th IEEE International Requirements Engineering Conference, (RE ’09) (pp. 37–46). Atlanta.
Cheng, B. H. C., & Atlee, J. M. (2007). Research directions in requirements engineering, Future of Software engineering, (FOSE) (pp. 285–303). Minneapolis.
Dumitru, H., Gibiec, M., Hariri, N., Cleland-Huang, J., Mobasher, B., Castro-Herrera, C., & Mirakhorli, M (2011). On-demand feature recommendations derived from mining public product descriptions, 33rd International Conference on Software Engineering (ICSE) (pp. 181–190). Waikiki, Honolulu.
Elvira Consortium (2002). Elvira, An environment for probabilistic graphical models, 1st International Workshop on Probabilistic Graphical Models (PGM02) (pp. 222–230). Cuenca.
de Freitas, F. G., & de Souza, J. T. (2011). Ten years of search based software engineering, A bibliometric analysis. Search Based Software Engineering Lecture Notes in Computer Science, 6956, 18–32.
de Gea, J. M. C., Nicolás, J., Alemán, J. L. F., Toval, A., Ebert, C., & Vizcaíno, A. (2012). Requirements engineering tools, Capabilities, survey and assessment. Information and Software Technology, 54(10), 1142–1157.
Greer, D., & Ruhe, G. (2004). Software release planning, an evolutionary and iterative approach. Information & Software Technology, 46(4), 243–253.CrossRef
Harman, M. (2012). The role of artificial intelligence in software engineering, Proceedings of the 1st International Workshop on Realizing AI Synergies in Software Engineering (pp. 1–6): IEEE.
Harman, M., Mansouri, S. A., & Zhang, Y. (2012). Search-based software engineering: Trends, techniques and applications. ACM Computing Surveys (CSUR), 45 (1), 11.CrossRef
Jensen, F. V. (2007). Information Science and Statistics. Bayesian Networks and Decision Graphs: Springer. corrected edition.
Karlsson, J., & Ryan, K. (1997). A cost-value approach for prioritizing requirements. IEEE Software, 14(5), 67–74.CrossRef
Kastro, Y., & Bener, A. B. (2008). A defect prediction method for software versioning. Software Quality Journal, 16(4), 543–562.CrossRef
Kjaerulff, U. B., & Madsen, A. L. (2007). Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, 1st edn: Springer.
Korb, K. B., & Nicholson, A. E. (2010). Bayesian Artificial Intelligence: CRC Press.
Kotonya, G., & Sommerville, I. (1998). Requirements engineering: Processes and techniques. New York: Wiley.
Lim, S. L., & Finkelstein, A. (2012). Stakerare: using social networks and collaborative filtering for large-scale requirements elicitation. IEEE Transactions on Software Engineering, 38(3), 707–735.CrossRef
Menzies, T., & Shepperd, M. (2012). Special issue on repeatable results in software engineering prediction. Empirical Software Engineering, 17(1), 1–17.CrossRef
Meziane, F., & Vadera, S. (2010). Artificial intelligence applications for improved software engineering development: New prospects. New York: IGI Global.
Misirli, A. T., & Bener, A. B. (2014). Bayesian networks for evidence-based decision-making in software engineering. IEEE Transactions on Software Engineering, 40(6), 533–554.CrossRef
Mısırlı, A. T., Bener, A. B., & Turhan, B. (2011). An industrial case study of classifier ensembles for locating software defects. Software Quality Journal, 19(3), 515–536.CrossRef
Nicolás, J., & Toval, A. (2009). On the generation of requirements specifications from software engineering models: A systematic literature review. Information and Software Technology, 51(9), 1291–1307.CrossRef
Orellana, F. J., Cañadas, J., del Águila, I. M., & Túnez, S. (2008). InSCo requisite - a web-based RM-tool to support hybrid software development, 10th International Conference on Enterprise Information Systems (ICEIS) (3-1) (pp. 326–329). Barcelona.
del Sagrado, J., & del Águila, I. M. (2010). Artificial intelligence applications for improved software engineering development, new prospects In Meziane, F., & Vadera, S. (Eds.), A Bayesian network for predicting the need for a requirements review, (pp. 106–128). New York: IGI Global.
del Sagrado, J., del Águila, I. M., & Orellana, F. J. (2011). Architecture for the use of synergies between knowledge engineering and requirements engineering. Lecture Notes in Computer Science, 7023, 213–222.CrossRef
del Sagrado, J., del Águila, I. M., & Orellana, F. J. (2012). Metaheuristic aided software features assembly, 20th European Conference on Artificial Intelligence (ECAI 2012), Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track (pp. 1009–1010). Montpellier.
del Sagrado, J., del Águila, I. M., & Orellana, F. J. (2015). Multi-objective ant colony optimization for requirements selection. Empirical Software Engineering, 20(3), 577–610.CrossRef
Shirabad, J. S., & Menzies, T. (2005). Predictor models in software engineering (promise), 27th international conference on Software engineering, (ICSE ’05) (pp. 692–692). New York: ACM.
Sommerville, I. (2011). Software engineering, 9 edn. Boston: Pearson Education.
Standish Group (2008). Chaos report. (2002).
Tosun, A., Bener, A., & Akbarinasaji, S. (2015). A systematic literature review on the applications of bayesian networks to predict software quality. Software Quality Journal, 1–33. cited By 0; Article in Press.
Wen, J., Li, S., Lin, Z., Hu, Y., & Huang, C. (2012). Systematic literature review of machine learning based software development effort estimation models. Information and Software Technology, 54(1), 41–59.CrossRef
Wiegers, K., & Beatty, J. (2013). Software requirements: Pearson Education.
Zhang, Y., Harman, M., & Mansouri, A. (2012). The SBSE repository: A repository and analysis of authors and research articles on search based software engineering. crestweb. cs. ucl. ac. uk/resources/sbse repository.
Metadaten
Titel
Stability prediction of the software requirements specification
verfasst von
José del Sagrado
Isabel M. del Águila
Publikationsdatum
08.04.2017
Verlag
Springer US
Erschienen in
Software Quality Journal / Ausgabe 2/2018
Print ISSN: 0963-9314
Elektronische ISSN: 1573-1367
DOI
https://doi.org/10.1007/s11219-017-9362-x

Weitere Artikel der Ausgabe 2/2018

Software Quality Journal 2/2018 Zur Ausgabe

OriginalPaper

Round-trip engineering with the Two-Tier Programming Toolkit

OriginalPaper

Integrating software quality models into risk-based testing

OriginalPaper

Investigating relationships between functional coupling and the energy efficiency of embedded software

OriginalPaper

An empirical study of crash-inducing commits in Mozilla Firefox

OriginalPaper

The effect of requests for user feedback on Quality of Experience

OriginalPaper

Considerations about quality in model-driven engineering

Premium Partner

    Bildnachweise