Requirements Prioritization Based on Benefit and Cost Prediction: An Agenda for Future Research

In early phases of the software cycle, requirements prioritization necessarily relies on the specified requirements and on predictions of benefit and cost of individual requirements. This paper presents results of a systematic review of literature, which investigates how existing methods approach the problem of requirements prioritization based on benefit and cost. From this review, it derives a set of under-researched issues which warrant future efforts and sketches an agenda for future research in this area

A situational approach for the definition and tailoring of a data-driven software evolution method

Successful software evolution heavily depends on the selection of the right features to be included in the next release. Such selection is difficult, and companies often report bad experiences about user acceptance. To overcome this challenge, there is an increasing number of approaches that propose intensive use of data to drive evolution. This trend has motivated the SUPERSEDE method, which proposes the collection and analysis of user feedback and monitoring data as the baseline to elicit and prioritize requirements, which are then used to plan the next release. However, every company may be interested in tailoring this method depending on factors like project size, scope, etc. In order to provide a systematic approach, we propose the use of Situational Method Engineering to describe SUPERSEDE and guide its tailoring to a particular context.Peer ReviewedPostprint (author's final draft

Local Government planning: from data to action

Decentralisation is built on the assu mption that decentralized governme nts are more knowledgeable about and responsive to the needs of the poor. This article ex amines the role of local governments in Kenya and the ways in which they make their decisions about the allocation of resources to deliver water and sanitation services. Two major challenges are identified: i) lack of data that accurately reveal which areas are most in need; and ii) inadequate instruments for planning, monitoring and evaluation. In tackling previous shortcomings, this study i) adopts a new specific appr oach for data collection at community level, and ii) exploits these data through simple composite indicator s as policy tools that assist local government with decision-making. It concludes that accurate and compre hensive data are the basis of effective targeting and prioritization, which are fundamental to sector planning.Peer ReviewedPostprint (published version

Scheduling with Fuzzy Methods

Nowadays, manufacturing industries -- driven by fierce competition and rising customer requirements -- are forced to produce a broader range of individual products of rising quality at the same (or preferably lower) cost. Meeting these demands implies an even more complex production process and thus also an appropriately increasing request to its scheduling. Aggravatingly, vagueness of scheduling parameters -- such as times and conditions -- are often inherent in the production process. In addition, the search for an optimal schedule normally leads to very difficult problems (NP-hard problems in the complexity theoretical sense), which cannot be solved effciently. With the intent to minimize these problems, the introduced heuristic method combines standard scheduling methods with fuzzy methods to get a nearly optimal schedule within an appropriate time considering vagueness adequately