Lightweight Replanning of Software Product Releases

PostprintThis is the accepted version of a paper published in Requirements Engineering. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.Citation for the original published paper (version of record):Svahnberg, M., Gorschek, T., Nguyen, T., Nguyen, M. (2015) Uni-REPM: a framework for requirements engineering process assessment. (will be inserted by the editor) Uni-REPM -a Framework for Requirements Engineering Process AssessmentMikael Svahnberg · Tony Gorschek · Thi Than Loan Nguyen · Mai Nguyen the date of receipt and acceptance should be inserted later Abstract Context. It has been shown that potential business benefits could be achieved by assessing and improving the requirements engineering (RE) process. However, process assessment models such as CMMI and ISO9000 only cover RE shallowly. Tailored models such as REGPG and REPM, on the other hand, do not cover market-driven requirements engineering. Other attempts, such as MDREPM covers market-driven requirements engineering, but correspondlingly neglects bespoke requirements engineering. Moreover, the area itself has evolved so practices that once were cutting edge are now commonplace. Objectives. In this article we develop and evaluate a unified requirements engineering process maturity model (Uni-REPM) that can be used in a market-driven as well as a bespoke context. This model is based on REPM, but has evolved to reflect contemporary requirements engineering practices. Methods. Uni-REPM is primarily created based on a systematic literature review of market-driven requirements engineering practices and a literature review of bespoke practices. Conclusions. Based on the results, Uni-REPM is formulated. The objective of Uni-REPM is twofold. Firstly, it is expected to be applicable for assessing the maturity of RE processes in various scenarios where an organization would use different development approaches. Secondly, it instructs practitioners about which RE practices to perform and their expected benefits. As an assessment instrument, Uni-REPM provides a simple and low cost solution for practitioners to identify the status of their RE process. As a guidance tool, Uni-REPM lessens the gap between theoretical and practical worlds by transferring the available RE technologies from research to industry practice.

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“…-RQ1. 3 Which practices from RQ1.1 and RQ1.2 are justified for market-driven RE by empirical validation and/or rationale?…”

Section: Revised Research Questionsmentioning

confidence: 99%

Uni-REPM: a framework for requirements engineering process assessment

et al. 2013

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“…Create new requirements for a feature(s) [8,[32][33][34][35][36][37] Change a requirement(s) in a feature [28,32,37] Remove a requirement from a feature(s) [38] Prioritize requirements [8,28,36,39,40] Allocate resources [32-34, 38, 39, 41] Allocate requirements to releases [8, 28, 33-36, 39, 40, 42] Select technology for set of requirements [34] None of the product planning approaches refers to analytics as a potential input to product planning. Analytics, however, provide essential data for guiding sales, marketing, and engineering of usability, performance, reliability, and quality of service.…”

Section: Releasementioning

confidence: 99%

Analytics for Product Planning: In-Depth Interview Study with SaaS Product Managers

Fotrousi

Izadyan²,

Fricker

2013

2013 IEEE Sixth International Conference on Cloud Computing

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Abstract-SaaS cloud computing, in contrast to packaged products, enables permanent contact between users of a software product and the product-owning company. When planning the development and evolution of a software product, a product manager depends on reliable information about feature attractiveness. So far, planning decisions were based on stakeholder opinion and the customer's willingness to buy. Whether or not a feature actually is used was out of consideration.Analytics that measure the interaction between users and the SaaS gives product managers unprecedented access to information about product usage. To understand whether and how SaaS analytics can be used for product planning decision, we performed 17 in-depth interviews with experienced managers of SaaS products and analyzed the results analyzed with a mixed-method strategy. The empirical results characterize the relevance of a broad range of analytics for product planning decisions, and the strengths and limitations of an analytics-based product planning approach.

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“…The work considered requirements problems as feature (component) subset selection problems, like Feather et al, presenting results for a single objective formulation applied to a real world data set: the Motorola Data Set. The work of AlBourae et al (2006) was focused more on the requirements change handling. That is, re-planning of the product release.…”

Section: Related Workmentioning

confidence: 99%

A study of the bi-objective next release problem

et al. 2010

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One important issue addressed by software companies is to determine which features should be included in the next release of their products, in such a way that the highest possible number of customers get satisfied while entailing the minimum cost for the company. This problem is known as the Next Release Problem (NRP). Since minimizing the total cost of including new features into a software package and maximizing the total satisfaction of customers are contradictory objectives, the problem has a multi-objective nature. In this work, we apply three state-of-the-art multi-objective metaheuristics (two genetic algorithms, NSGA-II and MOCell, and one evolutionary strategy, PAES) for solving NRP. Our goal is twofold: on the one hand, we are interested in analyzing the results obtained by these metaheuristics over a benchmark composed of six academic problems plus a real world data set provided by Motorola; on the other hand, we want to provide insight about the solution to the problem. The obtained results show three different kinds of conclusions: NSGA-II is the technique computing the highest number of optimal 30 Empir Software Eng (2011) 16:29-60 solutions, MOCell provides the product manager with the widest range of different solutions, and PAES is the fastest technique (but with the least accurate results). Furthermore, we have observed that the best solutions found so far are composed of a high percentage of low-cost requirements and of those requirements that produce the largest satisfaction on the customers as well.

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Lightweight Replanning of Software Product Releases

Cited by 17 publications

References 22 publications

Uni-REPM: a framework for requirements engineering process assessment

Uni-REPM: a framework for requirements engineering process assessment

Analytics for Product Planning: In-Depth Interview Study with SaaS Product Managers

A study of the bi-objective next release problem

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