Software Release Planning: An Evolutionary and Iterative Approach

Abstract To achieve higher flexibility and to better satisfy actual customer requirements, there is an increasing tendency to develop and deliver software in an incremental fashion. In adopting this process, requirements are delivered in releases and so a decision has to be made on which requirements should be delivered in which release. Three main considerations that need to be taken account of are the technical precedences inherent in the requirements, the typically conflicting priorities as determined by the representative stakeholders, as well as the balance between required and available effort. The technical precedence constraints relate to situations where one requirement cannot be implemented until another is completed or where one requirement is implemented in the same increment as another one. Stakeholder preferences may be based on the perceived value or urgency of delivered requirements to the different stakeholders involved. The technical priorities and individual stakeholder priorities may be in conflict and difficult to reconcile. This paper provides (i) a method for optimally allocating requirements to increments; (ii) a means of assessing and optimizing the degree to which the ordering conflicts with stakeholder priorities within technical precedence constraints; (iii) a means of balancing required and available resources for all increments; and (iv) an overall method called EVOLVE aimed at the continuous planning of incremental software development. The optimization method used is iterative and essentially based on a genetic algorithm. A set of the most promising candidate solutions is generated to support the final decision. The paper evaluates the proposed approach using a sample project.

Coastline configuration as a determinant of structure in larval assemblages

The richness and turnover of coastal larval pools set upper limits for biodiversity in coastal systems. For particular local systems, such as embayments, the characteristics of the local larval pool are determined by the relative contributions of locally produced and external larvae. The balance between these sources partially reflects the extent of tidal exchange and is hence related to system size and flushing time. Larvae of benthic marine invertebrates were sampled from 8 bays along the Irish coast to investigate the effect of coastline configuration on the characteristics of the larval pool. Flushing time explained 34.5% of the variability in species richness from a series of daily samples. Many of the potentially relevant environmental variables are correlated, limiting the potential for individual variables to be examined in isolation. We therefore used a principal components analysis to describe the major patterns in environmental variability across bays. The second principal component separated bays along a gradient of increasing depth, salinity, tidal range and flushing time. Scores along this component were generally better predictors of the larval pool than single variables, explaining as much as 61.2% of the variation in species richness, diversity and similarity between dates. Deeper bays, with more saline water and longer flushing times, tended to have richer and more diverse larval pools, with a greater consistency in species composition between sample dates. No relationship was found between environmental variables and larval abundance. Our results suggest that flushing time, particularly when in combination with topographic variables, chlorophyll, tidal range and salinity, may be a useful predictor for the richness and turnover of local larval pools.

Reduced-complexity iterative equalization for severe time-dispersive MIMO channels

In this paper, a reduced-complexity soft-interference-cancellation minimum mean-square-error.(SIC-MMSE) iterative equalization method for severe time-dispersive multiple-input-multiple-output (MIMO) channels is proposed. To mitigate the severe time dispersiveness of the channel, a single carrier with cyclic prefix is employed, and the equalization is per-formed in the frequency domain. This simplifies the challenging problem of equalization in MIMO channels due to both the intersymbol interference (ISI) and the coantenna interference (CAI). The proposed iterative algorithm works in two stages. The first stage estimates the transmitted frequency-domain symbols using a low-complexity SIC-MMSE equalizer. The second stage converts the estimated frequency-domain symbols in the time domain and finds their means and variances to incorporate in the SIC-MMSE equalizer in the next iteration. Simulation results show the bit-/symbol-error-rate performance of the SIC-MMSE equalizer, with and without coding, for various modulation schemes.