Computational support for the process of software requirement specification

The software industry has become more and more concerned with the appropriate application of activities that composes requirement engineering as a way to improve the quality of its products. In order to support these activities, several computational tools have been available in the market, although it is still possible to find a lack of resources related to some activities. In this context, this paper proposes the inclusion of a module to aid in the requirements specification to a tool called Requirements Elicitation Support Tool. This module allows to specify requirements in accordance with IEEE 830 standard, thus contributing to the documentation of the requirements established for a software system, besides supporting the learning of concepts related to the requirements specification, which improves the skills of users of the tool. © 2012 IEEE.

Requirement Risk Level Forecast Using Bayesian Networks Classifiers

Requirement engineering is a key issue in the development of a software project. Like any other development activity it is not without risks. This work is about the empirical study of risks of requirements by applying machine learning techniques, specifically Bayesian networks classifiers. We have defined several models to predict the risk level for a given requirement using three dataset that collect metrics taken from the requirement specifications of different projects. The classification accuracy of the Bayesian models obtained is evaluated and compared using several classification performance measures. The results of the experiments show that the Bayesians networks allow obtaining valid predictors. Specifically, a tree augmented network structure shows a competitive experimental performance in all datasets. Besides, the relations established between the variables collected to determine the level of risk in a requirement, match with those set by requirement engineers. We show that Bayesian networks are valid tools for the automation of risks assessment in requirement engineering.

Infrastructure project planning decision making : challenges for decision support system applications

Most infrastructure projects share the same characteristics in term of management aspects and shortcomings. Human factor is believed to be the major drawbacks due to the nature of unstructured problems which can further contribute to management conflicts. This growing complexity in infrastructure projects has shift the paradigm of policy makers to adopt Information Communication Technology (ICT) as a driving force. For this reason, it is vital to fully maximise and utilise the recent technologies to accelerate management process particularly in planning phase. Therefore, a lot of tools have been developed to assist decision making in construction project management. The variety of uncertainties and alternatives in decision making can be entertained by using useful tool such as Decision Support System (DSS). However, the recent trend shows that most DSS in this area only concentrated in model development and left few fundamentals of computing. Thus, most of them were found complicated and less efficient to support decision making within project team members. Due to the current incapability of many software aspects, it is desirable for DSS to provide more simplicity, better collaborative platform, efficient data manipulation and reflection to user needs. By considering these factors, the paper illustrates four challenges for future DSS development i.e. requirement engineering, communication framework, data management and interoperability, and software usability

Sustainable infrastructure project planning : progress in contemporary decision support tools

Most infrastructure project developments are complex in nature, particularly in the planning phase. During this stage, many vague alternatives are tabled - from the strategic to operational level. Human judgement and decision making are characterised by biases, errors and the use of heuristics. These factors are intangible and hard to measure because they are subjective and qualitative in nature. The problem with human judgement becomes more complex when a group of people are involved. The variety of different stakeholders may cause conflict due to differences in personal judgements. Hence, the available alternatives increase the complexities of the decision making process. Therefore, it is desirable to find ways of enhancing the efficiency of decision making to avoid misunderstandings and conflict within organisations. As a result, numerous attempts have been made to solve problems in this area by leveraging technologies such as decision support systems. However, most construction project management decision support systems only concentrate on model development and neglect fundamentals of computing such as requirement engineering, data communication, data management and human centred computing. Thus, decision support systems are complicated and are less efficient in supporting the decision making of project team members. It is desirable for decision support systems to be simpler, to provide a better collaborative platform, to allow for efficient data manipulation, and to adequately reflect user needs. In this chapter, a framework for a more desirable decision support system environment is presented. Some key issues related to decision support system implementation are also described.

Demand-compliant design

In this paper, we describe, in detail, a design method that assures that the designed product satisfies a set of prescribed demands while, at the same time, providing a concise representation of the design that facilitates communication in multidisciplinary design teams. This Demand Compliant Design (DeCoDe) method was in itself designed to comply with a set of demands. The demands on the method were determined by an analysis of some of the most widely used design methods and from the needs arising in the practice of design for quality. We show several modes of use of the DeCoDe method and illustrate with examples.

Practical decision support system evaluation for consultant selection

The widespread development of Decision Support System (DSS) in construction indicate that the evaluation of software become more important than before. However, it is identified that most research in construction discipline did not attempt to assess its usability. Therefore, little is known about the approach on how to properly evaluate a DSS for specific problem. In this paper, we present a practical framework that can be guidance for DSS evaluation. It focuses on how to evaluate software that is dedicatedly designed for consultant selection problem. The framework features two main components i.e. Sub-system Validation and Face Validation. Two case studies of consultant selection at Malaysian Department of Irrigation and Drainage were integrated in this framework. Some inter-disciplinary area such as Software Engineering, Human Computer Interaction (HCI) and Construction Project Management underpinned the discussion of the paper. It is anticipated that this work can foster better DSS development and quality decision making that accurately meet the client’s expectation and needs

The structured and practical approach in development of decision support system for consultant selection in public sector infrastructure project

Nowadays, most of the infrastructure development projects undertaken are complex in nature. Practically, public clients who do not have a good understanding of the design and management may suffer severe losses, especially for infrastructure projects. There is a need for luring the right consultant to secure client's investment in infrastructure developments. Throughout the project life cycle, consultants play vital role from the inception to completion stage of a project. A few studies in Malaysia show that infrastructure projects involving irrigation and drainage have experience problems such as poor workmanship, delay and cost overrun due to the consultant's inability or the client incompetence of recruiting consultants in time. This highlights the need of aided decision making and an efficient system to select the best consultant by using Decision Support System (DSS). On the other hand, recent trends reveal that most DSS in construction only concentrate on decision model development. These models are impractical and unused as they are complicated or difficult for laymen such as project managers to utilize. Thus, this research attempts to develop an efficient DSS for consultant selection namely consultDeSS. Driven by the motivation and research aims, this study deployed Design Science Research Methodology (DSRM) dominant with a combination of case studies at the Malaysian Department of Irrigation and Drainage (DID). Two real projects involving irrigation and drainage infrastructure were used to design, implement and evaluate the artefact. The 3-tier consultDeSS was revised after the evaluation and the design was significantly improved based on user feedback. By developing desirable tools that fit client's needs will enhance the productivity and minimize conflict within groups and organisations. The tool is more usable and efficient compared to previous studies in construction. Thus, this research has demonstrated a purposeful artefact with a practical and valid structured development approach that is applicable in a variety of problems in construction discipline.

Strategies to engage engineering students in group project work

Project focused group work is significant in developing social and personal skills as well as extending the ability to identify, formulate and solve engineering problems. As a result of increasing undergraduate class sizes, along with the requirement for many students to work part-time, group projects, peer and collaborative learning are seen as a fundamental part of engineering education. Group formation, connection to learning objectives and fairness of assessment has been widely reported as major issues that leave students dissatisfied with group project based units. Several strategies were trialled including a study of formation of groups by different methods across two engineering disciplines over the past 2 years. Other strategies involved a more structured approach to assessment practices of civil and electrical engineering disciplines design units. A confidential online teamwork management tool was used to collect and collate student self and peer assessment ratings and used for both formative feedback as well as assessment purposes. Student satisfaction and overall academic results in these subjects have improved since the introduction of these interventions. Both student and staff feedback highlight this approach as enhancing student engagement and satisfaction, improved student understanding of group roles, reducing number of dysfunctional groups whilst requiring less commitment of academic resources.

Multiphasic scaffolds for periodontal tissue engineering

For a successful clinical outcome, periodontal regeneration requires the coordinated response of multiple soft and hard tissues (periodontal ligament, gingiva, cementum, and bone) during the wound-healing process. Tissue-engineered constructs for regeneration of the periodontium must be of a complex 3-dimensional shape and adequate size and demonstrate biomechanical stability over time. A critical requirement is the ability to promote the formation of functional periodontal attachment between regenerated alveolar bone, and newly formed cementum on the root surface. This review outlines the current advances in multiphasic scaffold fabrication and how these scaffolds can be combined with cell- and growth factor-based approaches to form tissue-engineered constructs capable of recapitulating the complex temporal and spatial wound-healing events that will lead to predictable periodontal regeneration. This can be achieved through a variety of approaches, with promising strategies characterized by the use of scaffolds that can deliver and stabilize cells capable of cementogenesis onto the root surface, provide biomechanical cues that encourage perpendicular alignment of periodontal fibers to the root surface, and provide osteogenic cues and appropriate space to facilitate bone regeneration. Progress on the development of multiphasic constructs for periodontal tissue engineering is in the early stages of development, and these constructs need to be tested in large animal models and, ultimately, human clinical trials.

Automated feature engineering for HTTP tunnel detection

Generating discriminative input features is a key requirement for achieving highly accurate classifiers. The process of generating features from raw data is known as feature engineering and it can take significant manual effort. In this paper we propose automated feature engineering to derive a suite of additional features from a given set of basic features with the aim of both improving classifier accuracy through discriminative features, and to assist data scientists through automation. Our implementation is specific to HTTP computer network traffic. To measure the effectiveness of our proposal, we compare the performance of a supervised machine learning classifier built with automated feature engineering versus one using human-guided features. The classifier addresses a problem in computer network security, namely the detection of HTTP tunnels. We use Bro to process network traffic into base features and then apply automated feature engineering to calculate a larger set of derived features. The derived features are calculated without favour to any base feature and include entropy, length and N-grams for all string features, and counts and averages over time for all numeric features. Feature selection is then used to find the most relevant subset of these features. Testing showed that both classifiers achieved a detection rate above 99.93% at a false positive rate below 0.01%. For our datasets, we conclude that automated feature engineering can provide the advantages of increasing classifier development speed and reducing development technical difficulties through the removal of manual feature engineering. These are achieved while also maintaining classification accuracy.

Role of organic fluorine in crystal engineering

The design of compounds with novel and improved physico-chemical properties as advanced functional materials with a specific application spectrum requires the knowledge about possible supramolecular packing motifs and their experimental control in crystalline lattice. Besides the structure of the individual molecule, non-covalent interactions play a significant role in the determination of molecular conformation, along with the formation of three-dimensional supramolecular architecture in a crystal as a requirement for molecular recognition processes, and the related bioactivity. Involvement of functional groups will contribute to the formation of a predefined packing motif due to their well-defined interactions. The strength and directionality of these interactions create characteristic packing motifs, which can be used for the design of supramolecular arrangements by the development of appropriate strategies for the precise control of their topology. Most relevant of these non-covalent interactions are stacking interactions and hydrogen bonds, which have been subjects of extensive study in the last two decades. In recent literature, substantial efforts have been put in by various researchers towards the understanding of interactions involving organic fluorine and the role they play in generating different packing motifs which guides assembling of molecules in the crystal lattice.

Quantum Simulation of Dissipative Processes without Reservoir Engineering

We present a quantum algorithm to simulate general finite dimensional Lindblad master equations without the requirement of engineering the system-environment interactions. The proposed method is able to simulate both Markovian and non-Markovian quantum dynamics. It consists in the quantum computation of the dissipative corrections to the unitary evolution of the system of interest, via the reconstruction of the response functions associated with the Lindblad operators. Our approach is equally applicable to dynamics generated by effectively non-Hermitian Hamiltonians. We confirm the quality of our method providing specific error bounds that quantify its accuracy.

Requirements-based development of an improved engineering change management method

Engineering changes (ECs) are essential in complex product development, and their management is a crucial discipline for engineering industries. Numerous methods have been developed to support EC management (ECM), of which the change prediction method (CPM) is one of the most established. This article contributes a requirements-based benchmarking approach to assess and improve existing methods. The CPM is selected to be improved. First, based on a comprehensive literature survey and insights from industrial case studies, a set of 25 requirements for change management methods are developed. Second, these requirements are used as benchmarking criteria to assess the CPM in comparison to seven other promising methods. Third, the best-in-class solutions for each requirement are investigated to draw improvement suggestions for the CPM. Finally, an enhanced ECM method which implements these improvements is presented. © 2013 © 2013 The Author(s). Published by Taylor & Francis.