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.

Back to basics : empirical support for the importance of release planning in reducing sex offender recidivism

This article reports findings from a series of empirical studies investigating whether poor release planning might contribute to sex offender recidivism. A coding protocol was developed to measure the comprehensiveness of release planning which included items relating to accommodation, employment, pro-social support, community based treatment, and the Good Lives Model (T. Ward & C.A. Stewart, 2003) secondary goods. The protocol was retrospectively applied to groups of recidivist and non recidivist child molesters, matched on static risk level and time since release. As predicted, overall release planning was significantly poorer for recidivists compared to non recidivists. The accommodation, employment, and social support items combined to best predict recidivism, with predictive accuracy comparable to that obtained using static risk models. Results highlighted the importance of release planning in efforts to reduce sex offender recidivism. Implications for policy makers and community members are briefly discussed.

Planning helps : the impact of release planning on subsequent re-entry experiences of child sex offenders

Recent research has demonstrated that poor release planning is associated with sex offender recidivism; however, whether release planning correlates with actual re-entry experiences has not been investigated systematically. Accordingly, in the present study release planning was rated for 16 child sex offenders, and semi-structured interviews about re-entry experiences were conducted at one, three and six months following their release from prison. As expected, significant positive correlations were found between release planning and re-entry experiences across the follow-up period, indicating that higher-quality release planning is associated with more positive re-entry experiences. Accordingly, it can be assumed that re-entry experiences differ between recidivists and non-recidivists, and hence positive re-entry experiences contribute to a reduction in sex offender recidivism. The implications for the management of offender release, policy makers and society as a whole are discussed.

Mining mobile apps reviews to support release planning

The mobile apps market is a tremendous success, with millions of apps downloaded and used every day by users spread all around the world. For apps’ developers, having their apps published on one of the major app stores (e.g. Google Play market) is just the beginning of the apps lifecycle. Indeed, in order to successfully compete with the other apps in the market, an app has to be updated frequently by adding new attractive features and by fixing existing bugs. Clearly, any developer interested in increasing the success of her app should try to implement features desired by the app’s users and to fix bugs affecting the user experience of many of them. A precious source of information to decide how to collect users’ opinions and wishes is represented by the reviews left by users on the store from which they downloaded the app. However, to exploit such information the app’s developer should manually read each user review and verify if it contains useful information (e.g. suggestions for new features). This is something not doable if the app receives hundreds of reviews per day, as happens for the very popular apps on the market. In this work, our aim is to provide support to mobile apps developers by proposing a novel approach exploiting data mining, natural language processing, machine learning, and clustering techniques in order to classify the user reviews on the basis of the information they contain (e.g. useless, suggestion for new features, bugs reporting). Such an approach has been empirically evaluated and made available in a web-­‐based tool publicly available to all apps’ developers. The achieved results showed that the developed tool: (i) is able to correctly categorise user reviews on the basis of their content (e.g. isolating those reporting bugs) with 78% of accuracy, (ii) produces clusters of reviews (e.g. groups together reviews indicating exactly the same bug to be fixed) that are meaningful from a developer’s point-­‐of-­‐view, and (iii) is considered useful by a software company working in the mobile apps’ development market.

Conditional release: A less restrictive alternative to hospitalization?

Objective: This study examined conditional release - that is, involuntary outpatient commitment orders upon release from hospitalization - as a least restrictive alternative to psychiatric hospitalization in Victoria, Australia. Methods: Records were obtained from the Victorian Psychiatric Case Register for patients who experienced psychiatric hospitalization: between 1990 and 2000 a total of 8,879 patients were given conditional release and 16,094 were not. Results: Compared with the group that was hospitalized but did not receive a conditional release, the group that received a conditional release was more likely to have more prior hospitalizations of greater than average duration. Patients with schizophrenia were more likely to be given conditional release. Patients given conditional release experienced a care pattern involving briefer inpatient episodes (8.3 fewer days per episode), more inpatient days, and longer duration of restrictive care - that is, combined inpatient and conditional release periods (5.1 more days per month in care). Conclusions: For patients at risk of long-term hospitalization, conditional release may help to shorten inpatient episodes by providing a least restrictive alternative to continued hospitalization. However, patients who were given conditional release doubled the amount of days they spent under restrictive care, compared with the amount of time they previously spent in the hospital before entering a period of combined inpatient and conditional release commitment. Additional oversight may have led to more frequent hospitalization. This consequence raises new questions regarding the possible benefits of such extended oversight and new challenges for release planning using conditional release as a least restrictive method of care.

Ordering time to serve prisoners; a manual for the planning and administering of work release,

Mode of access: Internet.

Toxic chemical release inventory reporting forms and instructions : section 313 of the Emergency Planning and Right-to-Know Act : (Title III of the Superfund Amendments and Reauthorization Act of 1986).

"February 1999."

Tips for generating a media release and media coverage : how the media ate up my research on Aussie horror movies

The focus of this paper is preparing research for dissemination by mainstream print, broadcast, and online media. While the rise of the blogosphere and social media is proving an effective way of reaching niche audiences, my own research reached such an audience through traditional media. The first major study of Australian horror cinema, my PhD thesis A Dark New World: Anatomy of Australian Horror Films, generated strong interest from horror movie fans, film scholars, and filmmakers. I worked closely with the Queensland University of Technology’s (QUT) public relations unit to write two separate media releases circulated on October 13, 2008 and October 14, 2009. This chapter reflects upon the process of working with the media and provides tips for reaching audiences, particularly in terms of strategically planning outcomes. It delves into the background of my study which would later influence my approach to the media, the process of drafting media releases, and key outcomes and benefits from popularising research. A key lesson from this experience is that redeveloping research for the media requires a sharp writing style, letting go of academic justification, catchy quotes, and an ability to distil complex details into easy-to-understand concepts. Although my study received strong media coverage, and I have since become a media commentator, my experiences also revealed a number of pitfalls that are likely to arise for other researchers keen on targeting media coverage.