Analyzing Uncertainty in Release Planning: A Method and Experiment for Fixed-Date Release Cycles

Release planning—deciding what features to implement in upcoming releases of a software system—is a critical activity in iterative software development. Many release planning methods exist, but most ignore the inevitable uncertainty in estimating software development effort and business value. The article’s objective is to study whether analyzing uncertainty during release planning generates better release plans than if uncertainty is ignored. To study this question, we have developed a novel release planning method under uncertainty, called BEARS, that models uncertainty using Bayesian probability distributions and recommends release plans that maximize expected net present value and expected punctuality. We then compare release plans recommended by BEARS to those recommended by methods that ignore uncertainty on 32 release planning problems. The experiment shows that BEARS recommends release plans with higher expected net present value and expected punctuality than methods that ignore uncertainty, thereby indicating the harmful effects of ignoring uncertainty during release planning. These results highlight the importance of eliciting and analyzing uncertainty in software effort and value estimations and call for increased research in these areas.

Software Release Planning Using Grey Wolf Optimizer

This chapter presents the application of grey wolf optimizer in software release planning considering warranty based on the proposed mathematical model that measures reliability growth of software systems. Hence, optimal release and warranty time is determined while minimizing the overall software development cost. The software cost model is based on failure phenomenon modelled by incorporating fault removal efficiency, fault reduction factor, and error generation. The model has been validated on the fault dataset of ERP systems. Sensitivity analysis has been carried out to study the discrete changes in the cost parameter due to changes in optimal solution. The work significantly contributes to the literature by fulfilling gaps of reliability growth models, release problems considering warranty, and efficient ways for solving optimization problems. Further, the grey wolf optimizer result has been compared with genetic algorithm and particle swarm optimization techniques.

“You Can Get Prepared, But It’s Still Scary Walking Out The Door”: Exploring the Role of Dynamic Re-entry Factors in Release Planning for High-Risk Offenders

<p>The task of preparing high-risk prisoners for the multitude of challenges they will face once released is vital to their chances of successful re-entry. Recent research in New Zealand has found that developing good quality plans for life after prison is associated with reduced rates of reoffending after release – but how? One suggestion is that release plans help to ameliorate risks in offenders’ release environments. However, research examining how these risk factors are affected by the quality of release plans is scarce. This thesis investigates whether release planning has an indirect relationship with recidivism through its influence on dynamic risk and protective factors in re-entry, as measured by a risk management tool used by Community Probation Services in New Zealand: the Dynamic Risk Assessment for Offender Re-entry (DRAOR; Serin, 2007). A coding protocol to assess the quality of release plans was developed and retrospectively applied to a sample of 303 high-risk male parolees. Outcomes of interest were “short-term recidivism” (within 100 days of release) and “longer-term recidivism” (within one year of release) across four different indices. Results indicated that parolees who did not reoffend within the first 100 days of release had significantly better quality release plans than those who did reoffend. Better quality release plans also predicted greater stability in acute risk factors, and greater improvements in overall DRAOR scores, within the first 100 days of release. Logistic mediation analyses confirmed that release planning had an indirect relationship with both short-term and longer-term recidivism through its influence on DRAOR total scores. Together, these findings suggest that release planning may facilitate successful re-entry by reducing the impact of acute triggers or destabilisers in the release environment, thus protecting against a potential relapse. Theoretical and practical implications of these findings are discussed, along with limitations of the study and suggested directions for future research.</p>

“You Can Get Prepared, But It’s Still Scary Walking Out The Door”: Exploring the Role of Dynamic Re-entry Factors in Release Planning for High-Risk Offenders

<p>The task of preparing high-risk prisoners for the multitude of challenges they will face once released is vital to their chances of successful re-entry. Recent research in New Zealand has found that developing good quality plans for life after prison is associated with reduced rates of reoffending after release – but how? One suggestion is that release plans help to ameliorate risks in offenders’ release environments. However, research examining how these risk factors are affected by the quality of release plans is scarce. This thesis investigates whether release planning has an indirect relationship with recidivism through its influence on dynamic risk and protective factors in re-entry, as measured by a risk management tool used by Community Probation Services in New Zealand: the Dynamic Risk Assessment for Offender Re-entry (DRAOR; Serin, 2007). A coding protocol to assess the quality of release plans was developed and retrospectively applied to a sample of 303 high-risk male parolees. Outcomes of interest were “short-term recidivism” (within 100 days of release) and “longer-term recidivism” (within one year of release) across four different indices. Results indicated that parolees who did not reoffend within the first 100 days of release had significantly better quality release plans than those who did reoffend. Better quality release plans also predicted greater stability in acute risk factors, and greater improvements in overall DRAOR scores, within the first 100 days of release. Logistic mediation analyses confirmed that release planning had an indirect relationship with both short-term and longer-term recidivism through its influence on DRAOR total scores. Together, these findings suggest that release planning may facilitate successful re-entry by reducing the impact of acute triggers or destabilisers in the release environment, thus protecting against a potential relapse. Theoretical and practical implications of these findings are discussed, along with limitations of the study and suggested directions for future research.</p>

The Role of Release Planning in the Reintegration Experiences of High-Risk, Violent Offenders

<p>High-risk offenders face a variety of challenges when reintegrating back into the community, such as difficulty finding stable accommodation, obtaining employment, and accessing positive support networks. These reintegration challenges are a contributing factor to the high recidivism rates of this offender group and therefore necessitate dedicated attention to helping prepare high-risk offenders for such challenges. One method of aiding the transition from prison into the community is release planning, which simply involves helping an offender to plan for the fundamentals of life in the community. Release plan quality has been found to predict recidivism, suggesting that release plans are an appropriate target for intervention. The aim of the current thesis is to explore how release plans aid the transition from prison into the community and result in reduced recidivism rates. Study one explored the assumption that good quality release plans help offenders to have better experiences in the community after release (the ‘external’ pathway). Whilst the results suggested that better quality plans are related to better external experiences in general, a strong plan in a particular area (e.g., for accommodation) did not always translate into a good experience in that area. Additionally, experiences on parole only partially mediated the relationship between release plans and recidivism. Study two explored an alternative possibility: that good quality release plans have a psychological effect, resulting in higher levels of motivation to desist, self-efficacy, and prosocial identity (the ‘internal’ pathway). The results indicated that motivation to desist was the only variable mediating the relationship between release plans and recidivism (again, only partially). Study three, therefore, explored together the relative contributions of the external and internal pathways and found support for the external pathway, with external experiences fully mediating the relationship between release plans and recidivism. These external experiences also predicted levels of motivation to desist in the community. Finally, Study three also explored the role that Self-Determination Theory (SDT) may play in the role of release planning, with the aim of identifying contextual factors that help to improve the quality of release plans. After developing two measures to assess the specific SDT variables in the context of release planning, I found that the more offenders endorsed items indicating that they felt autonomous during release planning, the more intrinsically motivated they felt to create release plans, and the better quality their release plans were. The three studies together indicate that good quality release plans are related to offenders having better external experiences in the community, which in turn are related to reduced rates of reoffending. These positive external experiences are related to increased levels of motivation to desist in the community. There were measurement issues with the internal factors, suggesting that further research is required to better understand the role of psychological factors in the role of release planning. SDT has utility in the release planning process; the context in which offenders make release plans impacts on the quality of their plans. This thesis provides a strong argument for additional assistance in release planning being provided for offenders coming up for release.</p>

The Role of Release Planning in the Reintegration Experiences of High-Risk, Violent Offenders

<p>High-risk offenders face a variety of challenges when reintegrating back into the community, such as difficulty finding stable accommodation, obtaining employment, and accessing positive support networks. These reintegration challenges are a contributing factor to the high recidivism rates of this offender group and therefore necessitate dedicated attention to helping prepare high-risk offenders for such challenges. One method of aiding the transition from prison into the community is release planning, which simply involves helping an offender to plan for the fundamentals of life in the community. Release plan quality has been found to predict recidivism, suggesting that release plans are an appropriate target for intervention. The aim of the current thesis is to explore how release plans aid the transition from prison into the community and result in reduced recidivism rates. Study one explored the assumption that good quality release plans help offenders to have better experiences in the community after release (the ‘external’ pathway). Whilst the results suggested that better quality plans are related to better external experiences in general, a strong plan in a particular area (e.g., for accommodation) did not always translate into a good experience in that area. Additionally, experiences on parole only partially mediated the relationship between release plans and recidivism. Study two explored an alternative possibility: that good quality release plans have a psychological effect, resulting in higher levels of motivation to desist, self-efficacy, and prosocial identity (the ‘internal’ pathway). The results indicated that motivation to desist was the only variable mediating the relationship between release plans and recidivism (again, only partially). Study three, therefore, explored together the relative contributions of the external and internal pathways and found support for the external pathway, with external experiences fully mediating the relationship between release plans and recidivism. These external experiences also predicted levels of motivation to desist in the community. Finally, Study three also explored the role that Self-Determination Theory (SDT) may play in the role of release planning, with the aim of identifying contextual factors that help to improve the quality of release plans. After developing two measures to assess the specific SDT variables in the context of release planning, I found that the more offenders endorsed items indicating that they felt autonomous during release planning, the more intrinsically motivated they felt to create release plans, and the better quality their release plans were. The three studies together indicate that good quality release plans are related to offenders having better external experiences in the community, which in turn are related to reduced rates of reoffending. These positive external experiences are related to increased levels of motivation to desist in the community. There were measurement issues with the internal factors, suggesting that further research is required to better understand the role of psychological factors in the role of release planning. SDT has utility in the release planning process; the context in which offenders make release plans impacts on the quality of their plans. This thesis provides a strong argument for additional assistance in release planning being provided for offenders coming up for release.</p>

Optimal Release Planning Using Machine Learning and Linear Integer Programming for Ideas in a Crowdsourcing Platform

Obtaining and analyzing customer and product information from various sources has become a top priority for major competitive companies who are striving to keep up with the digital and technological progress. Therefore, the need for creating a crowdsourcing platform to collect ideas from different stakeholders has become a major component of a company’s digital transformation strategy. However, these platforms suffer from problems that are related to the voluminous and vast amount of data. Different large sets of data are being spurred in these platforms as time goes by that render them unbeneficial. The aim of this paper is to propose a solution on how to discover the most promising ideas to match them to the strategic decisions of a business regarding resource allocation and product development (PD) roadmap. The paper introduces a 2-stage filtering process that includes a prediction model using a Random Forest Classifier that predicts ideas most likely to be implemented and a resource allocation optimization model based on Integer Linear Programming that produces an optimal release plan for the predicted ideas. The model was tested using real data on an idea crowdsourcing platform that remains unnamed in the paper due to confidentiality. Our prediction model has proved to be 92% accurate in predicting promising ideas and our release planning optimization problem results were found out to be 85% accurate in producing an optimal release plan for ideas.