Relationship-based software attributes prioritization model for digital library sustainable development targets

Software attributes (SAs) represent the capability and usefulness of the software application in attaining sustainable development progress. To ensure a long-term positive impact on the aimed sustainable development targets (SDTs), an understanding of SAs relationship is required. The complexities of the study in this area significantly increased particularly with a huge gap in recognizing regulatory model and standards found from the literature. The previous research focusing on developing and designing new software technologies faced significant innovation and development effort gaps. Issues such as the rising cost, less strategy in purchasing as well as misallocation of the computing budget potentially lead towards lack of adopting new software technologies. Due to this phenomenon, sixty-five per cent of the countries participated in the UN2030 agenda are considered to lag in attaining their SDTs by the year 2030. This qualitative study, thus, proposed a relationship-based prioritization model in attaining the aimed SDTs for digital libraries. The focus was on deriving the SAs prioritization levels in the currently implemented software application focusing on the relationship of influences. In doing so, thirteen key attributes were generated via interviews with industry experts in this area. The pair-wise comparison and benefit-cost assessment tools were employed for data collection via structured interviews with nineteen digital libraries’ stakeholders at Malaysian higher learning institutions. The finding demonstrated similarities in prioritization levels for reliability, portability, and usability of digital library software (DLS). The reliability of DLS became the priority, followed by its portability as the fourth priority and its usability was the last priority. Meanwhile, the maintainability, functionality, and efficiency of DLS were identified in different priority levels. It also demonstrated that any changes or modification on the reliability of the DLS will influence the changes to SAs at a priority level lower than it was. Furthermore, the extent to which DLS portability will or will not influence other SAs was at priority fifth. Meanwhile, the usability of DLS did not influence any other SAs in attaining the aimed SDTs. The empirical findings of this study can be used as a guide to digital libraries towards better recognition of their current capabilities of DLS in attaining their aimed SDTs. The validated relationship-based prioritization model constructed could be used as a reference to provide direction for future research, particularly, in identifying good practices and lessons learned in this area

Dependency rank : método de priorização de requisitos baseado nas relações de dependência identificadas por PLN

Orientador: Prof. Dr. Andrey Ricardo PimentelDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 25/02/2019Inclui referências: p.102-105Resumo: Esta dissertação apresenta um método de priorização de requisitos de software baseada em relações de dependência entre funcionalidades. As técnicas de priorização de requisitos de software mais utilizadas atualmente dependem altamente de esforço humano para sua realização, sendo assim, o método proposto buscou diminuir a quantidade de esforço empregada, automatizando parte dessa tarefa, numa tentativa de fornecer maior agilidade e confiabilidade ao processo. Para isso, o método utilizou a documentação de requisitos de um projeto como base para extração dessas relações. Um protótipo que utiliza ferramentas de processamento de linguagem natural foi desenvolvido, sua aplicação teve o objetivo de reconhecer classes candidatas contidas em documentos de especificação de requisitos de software, escritos em formato de histórias de usuário, possibilitando, a partir disso, identificar links existentes entre as funcionalidades. Após essa análise, um ranking sugerido, que emprega como principal critério a priorização dos requisitos com maior número de dependências, é gerado. O método foi testado em dois experimentos, sendo um problema real já implementado e outro hipotético, que teve sua investigação auxiliada por profissionais. Os resultados dos experimentos mostraram que a estratégia implementada para identificação de classes candidatas atingiu, em seu melhor resultado, um F1 score para modelos de classificação de 0,857. Esse índice auxiliou o protótipo a classificar até 70% dos requisitos em intervalos idênticos aos obtidos por julgamento humano, tendo como principal desafio para desenvolvimentos futuros aumentar a carga de subjetividade do método. Palavras-chave: Priorização de requisitos de software. Interdependência entre requisitos. Processamento de linguagem natural. Histórias de usuário. Engenharia de software.Abstract: This dissertation presents a software requirements prioritization method based on dependency relations between features. The most commonly used software requirements prioritization techniques depend heavily on human effort in their performances, so the proposed method intended to reduce the amount of effort employed by automating part of the task in an attempt to improve agility and reliability to the process. Therefore the method used requirements documentations of a software project as a basis for extracting these relations. A prototype that uses natural language processing tools was developed, its application aimed to recognize candidate classes contained in software requirements specification documents, written as user stories, turning possible to identify existing links between the features. After this analysis, a suggested ranking, which employs as the main criterion to prioritize the requirements with greater number of dependencies, is generated. The method was tested in two experiments: a real problem already implemented and another hypothetical, which had its investigation aided by professionals. The results of the experiments showed that the candidate classes identification strategy implemented reached, in its best performance, 0.857 as F1 score for classification models. This index helped the prototype to classify up to 70% of the requirements at the same intervals to those obtained by human judgment. The main challenge for future developments is to increase the subjective analysis of the method. Keywords: Software requirements prioritization. Requirements interdependency. Natural language processing. User stories. Software engineering

Prioritisation of requests, bugs and enhancements pertaining to apps for remedial actions. Towards solving the problem of which app concerns to address initially for app developers

Useful app reviews contain information related to the bugs reported by the app’s end-users along with the requests or enhancements (i.e., suggestions for improvement) pertaining to the app. App developers expend exhaustive manual efforts towards the identification of numerous useful reviews from a vast pool of reviews and converting such useful reviews into actionable knowledge by means of prioritisation. By doing so, app developers can resolve the critical bugs and simultaneously address the prominent requests or enhancements in short intervals of apps’ maintenance and evolution cycles. That said, the manual efforts towards the identification and prioritisation of useful reviews have limitations. The most common limitations are: high cognitive load required to perform manual analysis, lack of scalability associated with limited human resources to process voluminous reviews, extensive time requirements and error-proneness related to the manual efforts. While prior work from the app domain have proposed prioritisation approaches to convert reviews pertaining to an app into actionable knowledge, these studies have limitations and lack benchmarking of the prioritisation performance. Thus, the problem to prioritise numerous useful reviews still persists. In this study, initially, we conducted a systematic mapping study of the requirements prioritisation domain to explore the knowledge on prioritisation that exists and seek inspiration from the eminent empirical studies to solve the problem related to the prioritisation of numerous useful reviews. Findings of the systematic mapping study inspired us to develop automated approaches for filtering useful reviews, and then to facilitate their subsequent prioritisation. To filter useful reviews, this work developed six variants of the Multinomial Naïve Bayes method. Next, to prioritise the order in which useful reviews should be addressed, we proposed a group-based prioritisation method which initially classified the useful reviews into specific groups using an automatically generated taxonomy, and later prioritised these reviews using a multi-criteria heuristic function. Subsequently, we developed an individual prioritisation method that directly prioritised the useful reviews after filtering using the same multi-criteria heuristic function. Some of the findings of the conducted systematic mapping study not only provided the necessary inspiration towards the development of automated filtering and prioritisation approaches but also revealed crucial dimensions such as accuracy and time that could be utilised to benchmark the performance of a prioritisation method. With regards to the proposed automated filtering approach, we observed that the performance of the Multinomial Naïve Bayes variants varied based on their algorithmic structure and the nature of labelled reviews (i.e., balanced or imbalanced) that were made available for training purposes. The outcome related to the automated taxonomy generation approach for classifying useful review into specific groups showed a substantial match with the manual taxonomy generated from domain knowledge. Finally, we validated the performance of the group-based prioritisation and individual prioritisation methods, where we found that the performance of the individual prioritisation method was superior to that of the group-based prioritisation method when outcomes were assessed for the accuracy and time dimensions. In addition, we performed a full-scale evaluation of the individual prioritisation method which showed promising results. Given the outcomes, it is anticipated that our individual prioritisation method could assist app developers in filtering and prioritising numerous useful reviews to support app maintenance and evolution cycles. Beyond app reviews, the utility of our proposed prioritisation solution can be evaluated on software repositories tracking bugs and requests such as Jira, GitHub and so on