Exploring the Landscape of Non-Functional Program Properties Using Spatial Analysis

  • Matthew PatrickEmail author
  • Yue Jia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9275)


Deciding on a trade-off between the non-functional properties of a system is challenging, as it is never possible to have complete information about what can be achieved. We may at first assume it is vitally important to minimise the processing requirements of a system, but if it is possible to halve the response time with only a small increase in computational power, would this cause us to change our minds? This lack of clarity makes program optimisation difficult, as it is unclear which non-functional properties to focus on improving. We propose to address this problem by applying spatial analysis techniques used in ecology to characterise and explore the landscape of non-functional properties. We can use these techniques to extract and present key information about the trade-offs that exist between non-functional properties, so that developers have a clearer understanding of the decisions they are making.


Spatial analysis Fitness landscapes Program optimisation 


  1. 1.
    Rosa, N.S., Justo, G.R.R., Cunha, P.R.F.: A framework for building non-functional software architectures. In: 16th ACM Symposium on Applied Computing, pp. 141–147. ACM, New York (2001)Google Scholar
  2. 2.
    Harman, M., Langdon, W.B., Jia, Y., White, D.R., Arcuri, A., Clark, J.A.: The GISMOE challenge: constructing the pareto program surface using genetic programming to find better programs. In: 25th IEEE/ACM International Conference on Automated Software Engineering, pp. 1–14. IEEE Press, New York (2012)Google Scholar
  3. 3.
    Langdon, W.B., Harman, M.: Optimising existing software with genetic programming. IEEE Trans. Evol. Comput. 19, 118–135 (2015)CrossRefGoogle Scholar
  4. 4.
    Lu, G., Li, J., Yao, X.: Fitness landscapes and problem difficulty in evolutionary algorithms: from theory to applications. In: Richter, H., Engelbrecht, A.P. (eds.) Recent Advances in the Theory and Application of Fitness Landscapes. ECC, vol. 6, pp. 133–162. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  5. 5.
    Imad, C., Slaheddine, S., Jihen, B., Rguibi-Idrissi, H., Dakki, M.: Factors affecting bird richness in a fragmented cork oak forest in Morocco. Acta Oecologica 35, 197–205 (2009)CrossRefGoogle Scholar
  6. 6.
    Gilligan, C.A., van den Bosch, F.: Epidemiologial models for invasion and persistence of pathogens. Annu. Rev. Phytopathol. 46, 385–418 (2008)CrossRefGoogle Scholar
  7. 7.
    Wittenberg, L., Malkinson, D., Beeri, O., Halutzy, A., Tesler, N.: Spatial and temporal patterns of vegetation recovery following sequences of forest fires in a mediterranean landscape, Mt. Carmel Israel. CATENA 71, 76–83 (2007)CrossRefGoogle Scholar
  8. 8.
    Fortin, M.-J., Dale, M.R.T.: Spatial Analysis: A Guide for Ecologists. Cambridge University Press, Cambridge (2005)Google Scholar
  9. 9.
    Diggle, P.J.: Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, 3rd edn. CRC Press, Boca Raton (2013) Google Scholar
  10. 10.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 888–905 (2000)CrossRefGoogle Scholar
  11. 11.
    Millard, A.G., White, D.R., Clark, J.A.: Searching for pareto-optimal randomised algorithms. In: Fraser, G., de Souza, J.T. (eds.) SSBSE 2012. LNCS, vol. 7515, pp. 183–197. Springer, Heidelberg (2012) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Plant SciencesUniversity of CambridgeCambridgeUK
  2. 2.Department of Computer ScienceUniversity College LondonLondonUK

Personalised recommendations