[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

Search-Based Software Project Management

  • Chapter
  • First Online:
Software Project Management in a Changing World

  • 4520 Accesses

Abstract

Project management presents the manager with a complex set of related optimisation problems. Decisions made can more profoundly affect the outcome of a project than any other activity. In the chapter, we provide an overview of Search-Based Software Project Management, in which search-based software engineering (SBSE) is applied to problems in software project management. We show how SBSE has been used to attack the problems of staffing, scheduling, risk, and effort estimation. SBSE can help to solve the optimisation problems the manager faces, but it can also yield insight. SBSE therefore provides both decision making and decision support. We provide a comprehensive survey of search-based software project management and give directions for the development of this subfield of SBSE.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 35.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Adamopoulos K, Harman, M, Hierons RM (2004) How to overcome the equivalent mutant problem and achieve tailored selective mutation using co-evolution. In: Proceedings of the 6th conference on genetic and evolutionary computation, pp 1338–1349

    Google Scholar 

  • Afzal W, Torkar R, Feldt R, Gorschek T (2014) Prediction of faults-slip-through in large software projects: an empirical evaluation. Software Qual J 22:51–86. doi:10.1007/s11219-013-9205-3

    Article  Google Scholar 

  • Aguilar-Ruiz JS, Ramos I, Riquelme JC, Toro M (2001) An evolutionary approach to estimating software development projects. Inf Softw Technol 43(14):875–882

    Article  Google Scholar 

  • Aguilar-Ruiz JS, Riquelme JC, Ramos I (2002) Natural evolutionary coding: an application to estimating software development projects. In: Proceedings of the 4th conference on genetic and evolutionary computation

    Google Scholar 

  • Akula B, Cusick J (2008) Impact of overtime and stress on software quality. In: Proceedings of the 4th international symposium on management, engineering, and informatics

    Google Scholar 

  • Alba E, Chicano F (2005) Management of software projects with GAs. In: Proceedings of the 6th metaheuristics international conference, pp 1152:1-6

    Google Scholar 

  • Alba E, Chicano F (2007) Software project management with GAs. Inf Sci 177(11):2380–2401

    Article  Google Scholar 

  • Alvarez-Valdes R, Crespo E, Tamarit JM, Villa F (2006) A scatter search algorithm for project scheduling under partially renewable resources. J Heuristics 12(1–2):95–113

    Article  MATH  Google Scholar 

  • Antoniol G, Di Penta M, Harman M (2004) A robust search-based approach to project management in the presence of abandonment, rework, error and uncertainty. In: Proceedings of the 10th international symposium on the software metrics, pp 172–183

    Google Scholar 

  • Antoniol G, Di Penta M, Harman M (2005) Search-based techniques applied to optimization of project planning for a massive maintenance project. In: Proceedings of the 21st IEEE international conference on software maintenance, pp 240–249

    Google Scholar 

  • Azar D (2010) A genetic algorithm for improving accuracy of software quality predictive models: a search-based software engineering approach. Int J Comput Intell Appl 9(2):125–136

    Article  MATH  MathSciNet  Google Scholar 

  • Barreto A, Barros M de O, Werner CM (2008) Staffing a software project: a constraint satisfaction and optimization-based approach. Comput Oper Res 35(10):3073–3089

    Google Scholar 

  • Beckers DG, van der Linden D, Smulders PG, Kompier MA, Taris TW, Geurts SA (2008) Voluntary or Involuntary? control over overtime and rewards for overtime in relation to fatigue and work satisfaction. Work Stress 22(1):33–50

    Article  Google Scholar 

  • Bouktif S, Kégl B, Sahraoui H (2002) Combining software quality predictive models: an evolutionary approach. In: Proceedings of the international conference on software maintenance, pp 385–392

    Google Scholar 

  • Bouktif S, Azar D, Precup D, Sahraoui H, Kégl B (2004) Improving rule set based software quality prediction: a genetic algorithm based approach. J Object Technol 3(4):227–241

    Article  Google Scholar 

  • Bouktif S, Sahraoui H, Antoniol G (2006) Simulated annealing for improving software quality prediction. In: Proceedings of the 8th conference on genetic and evolutionary computation, pp 1893–1900

    Google Scholar 

  • Braga PL, Oliveira ALI, Meira SRL (2008) A GA-based feature selection and parameters optimization for support vector regression applied to software effort estimation. In: Proceedings of the ACM symposium on applied computing, pp 1788–1792

    Google Scholar 

  • Briand L, Wieczorek I (2002) Software resource estimation. Encyclopedia Softw Eng 2:1160–1196

    Google Scholar 

  • Brooks FP Jr (1975) The mythical man month: essays on software engineering. Addison-Wesley Publishing Company, Reading, MA

    Google Scholar 

  • Burgess CJ, Lefley M (2001) Can genetic programming improve software effort estimation: a comparative evaluation. Inf Softw Technol 43(14):863–873

    Article  Google Scholar 

  • Chang CK (1994) Changing face of software engineering. IEEE Softw 11(1):4–5

    Google Scholar 

  • Chang CK, Chao C, Hsieh S-Y, Alsalqan Y (1994) SPMNet: a formal methodology for software management. In: Proceedings of the 18th international computer software and applications conference, p 57

    Google Scholar 

  • Chang CK, Chao C, Nguyen TT, Christensen M (1998) Software project management net: a new methodology on software management. In: Proceedings of the 22nd international computer software and applications conference, pp 534–539

    Google Scholar 

  • Chang CK, Christensen MJ, Zhang T (2001) Genetic algorithms for project management. Ann Softw Eng 11(1):107–139

    Article  MATH  Google Scholar 

  • Chao C, Komada J, Liu Q, Muteja M, Alsalqan Y, Chang C (1993) An application of genetic algorithms to software project management. In: Proceedings of the 9th international advanced science and technology, pp 247–252

    Google Scholar 

  • Chen WN, Zhang J (2013) Ant colony optimization for software project scheduling and staffing with an event-based scheduler. IEEE Trans Softw Eng 39(1):1–17

    Article  Google Scholar 

  • Chicano F, Luna F, Nebro AJ, Alba E (2011) Using multi objective metaheuristics to solve the software project scheduling problem. In: Proceedings of the 13th conference on genetic and evolutionary computation, pp 1915–1922

    Google Scholar 

  • Chiu NH, Huang S (2007) The adjusted analogy-based software effort estimation based on similarity distances. J Syst Softw 80(4):628–640

    Article  Google Scholar 

  • Conte D, Dunsmore H, Shen V (1986) Software engineering metrics and models. The Benjamin/Cummings Publishing Company, Redwood City, CA

    Google Scholar 

  • Corazza A, Di Martino S, Ferrucci F, Gravino C, Sarro F, Mendes E (2010) How effective is Tabu search to configure support vector regression for effort estimation?. In: Proceedings of the 6th international conference on predictive models in software engineering, pp 4:1-10

    Google Scholar 

  • Corazza A, Di Martino S, Ferrucci F, Gravino C, Sarro F, Mendes E (2013) Using Tabu search to configure support vector regression for effort estimation. Empir Softw Eng 18(3):506–546

    Article  Google Scholar 

  • Cortellessa V, Marinelli F, Potena P (2008) An optimization framework for “build-or-buy” decisions in software architecture. Comput Oper Res 35(10):3090–3106

    Article  MATH  Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  • Di Martino S, Ferrucci F, Gravino C, Sarro F (2011) A genetic algorithm to configure support vector machines for predicting fault-prone components. In: PROFES 2011. Lecture notes in computer science, vol 6759. Springer, Heidelberg, p 247

    Google Scholar 

  • Di Penta M, Antoniol G, Harman M, Qureshi F (2007) The effect of communication overhead on software maintenance project staffing: a search-based approach. In: Proceedings of the 23rd IEEE international conference on software maintenance, pp 315–324

    Google Scholar 

  • Di Penta M, Antoniol G, Harman M (2011) The use of search-based optimization techniques to schedule and staff software projects: an approach and an empirical study. Softw Pract Exp 41(5):495–519

    Article  Google Scholar 

  • Dolado JJ (2001) On the problem of the software cost function. Inf Softw Technol 43(1):61–72

    Article  Google Scholar 

  • Doval D, Mancordis SB, Mitchell S (1998) Automatic clustering of software system using a genetic algorithm. In: Proceedings of the 9th international workshop software technology and engineering practice, pp 73–81

    Google Scholar 

  • Everson RM, Fieldsend JE (2006) Multiobjective optimization of safety related systems: an application to short-term conflict alert. IEEE Trans Evol Comput 10(2):187–198

    Article  MathSciNet  Google Scholar 

  • Faheem A, Bouktif S, Serhani A, Khalil I (2008) Integrating function point project information for improving the accuracy of effort estimation. In: Proceedings of the international conference on advanced engineering computing and applications in sciences, pp 193–219

    Google Scholar 

  • Ferrucci F, Gravino C, Mendes E, Oliveto R, Sarro F (2010a) Investigating Tabu search for Web effort estimation. In: Proceedings of the 36th EUROMICRO conference on software engineering and advanced applications, pp 350–357

    Google Scholar 

  • Ferrucci F, Gravino C, Oliveto R, Sarro F (2010b) Estimating software development effort using Tabu search. In: Proceedings of the 12th international conference on enterprise information systems, vol 1. pp 236–241

    Google Scholar 

  • Ferrucci F, Gravino C, Oliveto R, Sarro F (2010c) Genetic programming for effort estimation: an analysis of the impact of different fitness functions. In: Proceedings of the 2nd international symposium on search based software engineering, pp 89–98

    Google Scholar 

  • Ferrucci F, Gravino C, Oliveto R, Sarro F (2010d) Using evolutionary based approaches to estimate software development effort. In: Chis M (ed) Evolutionary computation and optimization algorithms in software engineering: applications and techniques. IGI Global, Hershey, PA, pp 13–28

    Chapter  Google Scholar 

  • Ferrucci F, Gravino C, Sarro F (2011) How multi-objective genetic programming is effective for software development effort estimation? In: Proceedings of the 3rd international symposium on search based software engineering. Lecture notes in computer science, vol 6956. Springer, Heidelberg, pp 274–275

    Google Scholar 

  • Ferrucci F, Harman M, Ren J, Sarro F (2013) Not going to take this anymore: multi-objective overtime planning for software engineering projects. In: Proceedings of the 35th IEEE international conference on software engineering, pp 462–471

    Google Scholar 

  • Finkelstein A, Harman M, Mansouri S. A, Ren J, Zhang Y (2008) “Fairness Analysis” in requirements assignments. In: Proceedings of the 16th IEEE international requirements engineering conference, pp 115–124

    Google Scholar 

  • Finkelstein A, Harman M, Mansouri SA, Ren J, Zhang Y (2009) A search based approach to fairness analysis in requirement assignments to aid negotiation, mediation and decision making. Requir Eng 14(4):231–245

    Article  Google Scholar 

  • Gueorguiev S, Harman M, Antoniol G (2009) Software project planning for robustness and completion time in the presence of uncertainty using multi objective search-based software engineering. In: Proceedings of the genetic and evolutionary computation conference, pp 1673–1680

    Google Scholar 

  • Harman M (2007a) The current state and future of search-based software engineering. In: Proceedings of the conference on future of software engineering, pp 342–357

    Google Scholar 

  • Harman M (2007b) Search-based software engineering for program comprehension. In: Proceedings of the 15th IEEE international conference on program comprehension, pp 3–13

    Google Scholar 

  • Harman M (2010a) The relationship between search-based software engineering and predictive modelling. In: Proceedings of the 6th international conference on predictive models in software engineering, pp 1

    Google Scholar 

  • Harman M (2010b) Why the virtual nature of software makes it ideal for search-based optimization. In: Proceedings of the 13th international conference on fundamental approaches to software engineering, pp 1–12

    Google Scholar 

  • Harman M (2011) Making the case for MORTO: multi objective regression test optimization. In: Proceedings of the 1st international workshop on regression testing, pp 111–114

    Google Scholar 

  • Harman M, Clark JA (2004) Metrics are fitness functions too. In: Proceedings of the 10th international symposium on software metrics, pp 58–69

    Google Scholar 

  • Harman M, Jones BF (2001) Search-based software engineering. Inf Softw Technol 43(14):833–839

    Article  Google Scholar 

  • Harman M, Tratt L (2007) Pareto optimal search-based refactoring at the design level. In: Proceedings of the 9th conference on genetic and evolutionary computation, pp 1106–1113

    Google Scholar 

  • Harman M, Hierons R, Proctor M (2002) A new representation and crossover operator for search-based optimization of software modularization. In: Proceedings of the 4th conference on genetic and evolutionary computation, pp 1351–1358

    Google Scholar 

  • Harman M, Lakhotia K, McMinn P (2007) A multi-objective approach to search-based test data generation. In: Proceedings of the 9th conference on genetic and evolutionary computation, pp 1098–1105

    Google Scholar 

  • Harman M, Krinke J, Ren J, Yoo S (2009) Search-based data sensitivity analysis applied to requirement engineering. In: Proceedings of the 11th conference on genetic and evolutionary computation, pp 1681–1688

    Google Scholar 

  • Harman M, McMinn P, Teixeira de Souza J, Yoo S (2010) Search-based software engineering: techniques, taxonomy, tutorial. LASER Summer School 2010, pp 1–59

    Google Scholar 

  • Harman M, Burke E, Clark JA, Yao X (2012a) Dynamic adaptive search-based software engineering. In: Proceedings of the 6th IEEE international symposium on empirical software engineering and measurement, pp 1–8

    Google Scholar 

  • Harman M, Mansouri A, Zhang Y (2012b) Search-based software engineering: trends, techniques and applications. ACM Comput Surv 45(1):11–75

    Article  Google Scholar 

  • Hericko M, Zivkovic A, Rozman I (2008) An approach to optimizing software development team size. Inf Process Lett 108(3):101–106

    Article  MathSciNet  Google Scholar 

  • Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, MI

    Google Scholar 

  • Huang SJ, Chiu NH, Chen LW (2008) Integration of the grey relational analysis with genetic algorithm for software effort estimation. Eur J Oper Res 188(3):898–909

    Article  MATH  Google Scholar 

  • ISBSG (2013) Data repository. Available at http://www.isbsg.org

  • Jarillo G, Succi G, Pedrycz W, Reformat M (2011) Analysis of software engineering data using computational intelligence techniques. In: Proceedings of the 7th international conference on object oriented information systems, pp 133–142

    Google Scholar 

  • Jiang H, Chang CK, Xia J, Cheng S (2007) A history-based automatic scheduling model for personnel risk management. In: Proceedings of the 31st computer software and application conference, pp 361–364

    Google Scholar 

  • Kang D, Jung J, Bae DH (2011) Constraint-based human resource allocation in software projects. Softw Pract Exp 41(5):551–577

    Article  Google Scholar 

  • Kapur P, Ngo-The A, Ruhe G, Smith A (2008) Optimized staffing for product releases and its application at chartwell technology. J Softw Maint Evol Res Pract 20(5):365–386

    Article  Google Scholar 

  • Khoshgoftaar TM, Liu Y (2007) A multi-objective software quality classification model using genetic programming. IEEE Trans Reliab 56(2):237–245

    Article  Google Scholar 

  • Khoshgoftaar TM, Liu Y, Seliya N (2003) Genetic programming-based decision trees for software quality classification. In: Proceedings of the 15th international conference on tools with artificial intelligence, pp 374–383

    Google Scholar 

  • Kiper JD, Feather MS, Richardson J (2007) Optimizing the V&V process for critical systems. In: Proceedings of the 9th conference on genetic and evolutionary computation, p 1139

    Google Scholar 

  • Kirsopp C, Shepperd MJ, Hart J (2002) Search heuristics, case-based reasoning and soft- ware project effort prediction. In Proceedings of the genetic and evolutionary computation conference, pp 1367–1374

    Google Scholar 

  • Kitchenham B, Pickard LM, MacDonell SG, Shepperd MJ (2001) What accuracy statistics really measure. IEEE Proc Softw 148(3):81–85

    Article  Google Scholar 

  • Kleppa E, Sanne B, Tell GS (2008) Working overtime is associated with anxiety and depression: the Hordaland health study. J Occup Environ Med 50(6):658–666

    Article  Google Scholar 

  • Koch S, Mitlöhner J (2009) Software project effort estimation with voting rules. Decis Support Syst 46(4):895–901

    Article  Google Scholar 

  • Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA

    MATH  Google Scholar 

  • Lefley M, Shepperd MJ (2003) Using genetic programming to improve software effort estimation based on general data sets. In: Proceedings of the 5th genetic and evolutionary computation conference, pp 2477–2487

    Google Scholar 

  • Li YF, Xie M, Goh TN (2009) A study of project selection and feature weighting for analogy based software cost estimation. J Syst Softw 82(2):241–252

    Article  Google Scholar 

  • Liu Y, Khoshgoftaar TM (2001) Genetic programming model for software quality classification. In: Proceedings of the 6th IEEE international symposium on high-assurance systems engineering: special topic: impact of networking, pp 127–136

    Google Scholar 

  • Liu Y, Khoshgoftaar TM (2003) Building decision tree software quality classification models using genetic programming. In: Proceedings of the 5th genetic and evolutionary computation conference, pp 1808–1809

    Google Scholar 

  • Liu Y, Khoshgoftaar T (2004) Reducing overfitting in genetic programming models for software quality classification. In: Proceedings of the 8th IEEE international symposium on high assurance systems engineering, pp 56–65

    Google Scholar 

  • Lokan C (2005) What should you optimize when building an estimation model? In: Proceedings of the 11th IEEE international symposium on metrics, pp 34

    Google Scholar 

  • Luna F, Chicano JF, Alba E (2012) Robust solutions for the software project scheduling problem: a preliminary analysis. Int J Metaheuristic 2(1):56–79

    Article  MathSciNet  Google Scholar 

  • Mann C, Maurer F (2005) A case study on the impact of scrum on overtime and customer satisfaction. In: Agile development conference, pp 70–79

    Google Scholar 

  • McMinn P (2004) Search-based software test data generation: a survey. Softw Test Verif Reliab 14(2):105–156

    Article  Google Scholar 

  • Mendes E (2009) Web cost estimation and productivity benchmarking. software engineering, vol 5413, Lecture notes in computer science. Springer, Heidelberg, pp 194–222

    Google Scholar 

  • Menzies, T, Caglayan B, Kocaguneli E, Krall J, Peters F, Turhan B (2012) The PROMISE repository of empirical software engineering data. http://promisedata.googlecode.com

  • Minku LL, Yao X (2012) Software effort estimation as a multi-objective learning problem. ACM Trans Softw Eng Methodol 22(4):35:1–35:32

    Google Scholar 

  • Minku LL, Yao X (2013) An analysis of multi-objective evolutionary algorithms for training ensemble models based on different performance measures in software effort estimation. In: Proceedings of the 9th international conference on predictive models in software engineering, pp 8:1–8:10

    Google Scholar 

  • Minku LL, Sudholt D, Yao X (2012) Evolutionary algorithms for the project scheduling problem: runtime analysis and improved design. In: Proceedings of the genetic and evolutionary computation conference, pp 1221–1228

    Google Scholar 

  • Minku LL, Sudholt D, Yao X (2013) Improved evolutionary algorithm design for the project scheduling problem based on runtime analysis. IEEE Trans Softw Eng 40:83–102. doi:10.1109/TSE.2013.52

    Article  Google Scholar 

  • Mitchell BS, Mancoridis S (2002) Using heuristic search techniques to extract design abstractions from source code. In: Proceedings of the genetic and evolutionary computation conference, pp 1375–1382

    Google Scholar 

  • Nishikitani M, Nakao M, Karita K, Nomura K, Yano E (2005) Influence of overtime work, sleep duration, and perceived job characteristics on the physical and mental status of software engineers. Ind Health 43(4):623–629

    Article  Google Scholar 

  • Papatheocharous E, Andreou SA (2009) Hybrid computational models for software cost prediction: an approach using artificial neural networks and genetic algorithms, vol 19, Lecture notes in business information processing. Springer, Heidelberg, pp 87–100

    Google Scholar 

  • Rahman MM, Sohan SM, Maurer F, Ruhe G (2010) Evaluation of optimized staffing for feature development and bug fixing. In: Proceedings of the ACM-IEEE international symposium on empirical software engineering and measurement, p 42

    Google Scholar 

  • Räihä O (2010) A survey on search-based software design. Comput Sci Rev 4(4):203–249

    Article  Google Scholar 

  • Ren J, Harman M, Di Penta M (2011) Cooperative co-evolutionary optimization on software project staff assignments and job scheduling. In: Proceedings of the 3rd international symposium on search based software engineering, pp 127–141

    Google Scholar 

  • Rodriguez D, Ruiz M, Riquelme JC, Harrison R (2011) Multiobjective simulation optimisation in software project management. In: Proceedings of the 13th conference on genetic and evolutionary computation, pp 1883–1890

    Google Scholar 

  • Sarro F (2011) Search-based approaches for software development effort estimation. In: Proceedings of the 12th international conference on product-focused software development and process improvement (doctoral symposium), pp 38–43

    Google Scholar 

  • Sarro F (2013) Search-based approaches for software development effort estimation. Ph.D. thesis,. University of Salerno, Italy. http://www0.cs.ucl.ac.uk/staff/F.Sarro/

  • Sarro F, Di Martino S, Ferrucci F, Gravino C (2012a) A further analysis on the use of genetic algorithm to configure support vector machines for inter-release fault prediction. In: Proceedings of the 27th annual ACM symposium on applied computing, pp 1215–1220

    Google Scholar 

  • Sarro F, Ferrucci F, Gravino C (2012b) Single and multi objective genetic programming for software development effort estimation. In: Proceedings of the 27th annual ACM symposium on applied computing, pp 1221–1226

    Google Scholar 

  • Shackelford MRN (2007) Implementation issues for an interactive evolutionary computation system. In: Proceedings of the genetic and evolutionary computation conference, pp 2933–2936

    Google Scholar 

  • Shackelford MRN, Corne DW (2001) Collaborative evolutionary multi-project resource scheduling. In: Proceedings of the congress on evolutionary computation, vol 2. pp 1131–1138

    Google Scholar 

  • Shan Y, McKay RI, Lokan CJ, Essam DL (2002) Software project effort estimation using genetic programming. In: Proceedings of international conference on communications circuits and systems, pp 1108–1112

    Google Scholar 

  • Shepperd MJ, MacDonell SJ (2012) Evaluating prediction systems in software project estimation. Inf Softw Technol 54(8):820–827

    Article  Google Scholar 

  • Shukla KK (2000) Neurogenetic prediction of software development effort. Inf Softw Technol 42(10):701–713

    Article  Google Scholar 

  • Simons CL, Parmee IC (2008) User-centered, evolutionary search in conceptual software design. In: Proceedings of the IEEE congress on evolutionary computation, pp 869–876

    Google Scholar 

  • Simons CL, Parmee IC (2012) Elegant object-oriented software design via interactive evolutionary computation. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):1797–1805

    Article  Google Scholar 

  • Song L, Minku LL, Yao X (2013) The impact of parameter tuning on software effort estimation using learning machines. In: Proceedings of the 9th international conference on predictive models in software engineering

    Google Scholar 

  • Stylianou C, Andreou AS (2013) A multi-objective genetic algorithm for intelligent software project scheduling and team staffing. Intell Decis Technol 7(1):59–80

    Google Scholar 

  • Stylianou C, Gerasimou S, Andreou AS (2012) A novel prototype tool for intelligent software project scheduling and staffing enhanced with personality factors. In: Proceedings of the 24th international conference on tools with artificial intelligence, pp 277–284

    Google Scholar 

  • Xiao J, Osterweil LJ, Wang Q, Li M (2010a) Dynamic resource scheduling in disruption-prone software development environments. In: Proceedings of the 13th conference on fundamental approaches to software engineering, pp 107–122

    Google Scholar 

  • Xiao J, Osterweil LJ, Wang Q, Li M (2010b) Disruption-driven resource rescheduling in software development processes. In: New modeling concepts for today’s software processes. Lecture notes in computer science, vol 6195. Springer, Heidelberg, pp 234–247

    Google Scholar 

  • Xiao J, Osterweil LJ, Chen J, Wang Q, Li M (2013) Search-based risk mitigation planning in project portfolio management. In: Proceedings of the 2013 international conference on software and system process, pp 146–155

    Google Scholar 

  • Yoo S, Harman M (2012) Regression testing minimization, selection and prioritization: a survey. Softw Test Verif Reliab 22(2):67–120

    Article  Google Scholar 

  • Yourdon E (1997) Death March: the complete software developer’s guide to surviving ‘mission impossible’ projects. Prentice-Hall, Upper Saddle River, NJ

    Google Scholar 

  • Zhang Y (2013) SBSE paper repository. http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/

  • Zhang Y, Harman M, Mansouri SA (2007) The multi-objective next release problem. In: Proceedings of the 9th conference on genetic and evolutionary computation, pp 1129–1137

    Google Scholar 

  • Zhang Y, Finkelstein A, Harman M (2008) Search-based requirements optimisation: existing work and challenges. In Proceedings of the 14th international conference on requirements engineering: foundation for software quality, pp 88–94

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DISTRA, University of Salerno, Salerno, Italy

    Filomena Ferrucci

  2. CREST, Department of Computer Science, University College London, London, UK

    Mark Harman & Federica Sarro

Authors
  1. Filomena Ferrucci
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark Harman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Federica Sarro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Federica Sarro .

Editor information

Editors and Affiliations

  1. University of Calgary, Calgary, Alberta, Canada

    Günther Ruhe

  2. Blekinge Institute of Technology, Karlskrona, Sweden

    Claes Wohlin

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Ferrucci, F., Harman, M., Sarro, F. (2014). Search-Based Software Project Management. In: Ruhe, G., Wohlin, C. (eds) Software Project Management in a Changing World. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55035-5_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-55035-5_15

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-55034-8

  • Online ISBN: 978-3-642-55035-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation