The next release problem

By Anthony J. Bagnall, Victor J. Rayward-Smith, Ian M. Whittley. Information and Software Technology, 2001

353 citations recorded by the source corpus.

Companies developing and maintaining complex software systems need to determine the features that should be added to their system as part of the next release. They will wish to select these features to ensure the demands of their client base are satisfied as much as possible while at the same time ensuring that they themselves have the resources to undertake the necessary development. This situation is modelled in this paper and the problem of selecting an optimal next release is shown to be NP-hard. The use of various modern heuristics to find a high quality but possibly suboptimal solution is described. Comparative studies of these heuristics are given for various test cases.

Subfields: Computer Networks and Communications, Industrial and Manufacturing Engineering, Software

View Paper: https://www.semanticscholar.org/paper/febcb33c6f23babae63a1527748c30edc2c285b3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Shin Yoo
2. Mark Harman
3. Lionel Claude Briand
4. Andrea Arcuri
5. Yuanyuan Zhang
6. Phil McMinn

In Industrial and Manufacturing Engineering

1. S. Afshin Mansouri
2. Mark Harman
3. Lionel Claude Briand
4. Andrea Arcuri
5. Yuanyuan Zhang
6. Phil McMinn

In Software

1. Mark Harman
2. Andrea Arcuri
3. Phil McMinn
4. Shin Yoo
5. Lionel Claude Briand
6. Gordon Fraser
7. Jerffeson Teixeira de Souza
8. Yuanyuan Zhang

Citations in the corpus

• The Current State and Future of Search Based Software Engineering (2007, methodology): “The next release problem, taken from [5]”
• Search-based software engineering: Trends, techniques and applications (2012, neutral): “[Bagnall et al. 2001] suggested the term Next Release Problem (NRP) for requirements release planning and described various metaheuristic optimization algorithms, including greedy algorithms, branch and bound, SA and HC.”
• A practical guide for using statistical tests to assess randomized algorithms in software engineering (2011, methodology): “rithms include software testing [41], requirement engineering [8], project planning and cost estimation [2], bug fixing [7], automated maintenance [43], service-oriented software engineering [9], compiler optimisation [11] and quality assessment [32].”
• Search Based Software Engineering: A Comprehensive Analysis and Review of Trends Techniques and Applications (2009, background): “[47] formulated the ‘Next Release Problem (NRP)’ as a search problem.”
• A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering (2014, methodology): “Appli-34 cations of search algorithms include software testing [81], requirement engineering [18], project planning and cost estimation [2], bug fixing [14], automated maintenance [84], service-oriented software engineering [22], compiler optimisation [26] and quality assessment [67].”
• Search Based Software Engineering: Techniques, Taxonomy, Tutorial (2010, methodology): “[13] considered maximising the customer satisfaction (by inclusion of their demanded requirements in the next version) while not exceeding the company’s budget.”