Representing reusable software

Many methods for representing software components for reuse have been proposed. These include traditional library and information science methods, knowledge-based methods, and hypertext. The paper surveys and categorizes these methods, and discusses systems in which they have been used. A definition and conceptual framework of software reuse representations is proposed that relates these methods to each other and to the software life-cycle.     

Synthesis of probabilistic models for quality-of-service software engineering

An increasingly used method for the engineering of software systems with strict quality-of-service (QoS) requirements involves the synthesis and verification of probabilistic models for many alternative architectures and instantiations of system parameters. Using manual trial-and-error or simple heuristics for this task often produces suboptimal models, while the exhaustive synthesis of all possible models is typically intractable. The EvoChecker search-based software engineering approach presented in our paper addresses these limitations by employing evolutionary algorithms to automate the model synthesis process and to significantly improve its outcome. EvoChecker can be used to synthesise the Pareto-optimal set of probabilistic models associated with the QoS requirements of a system under design, and to support the selection of a suitable system architecture and configuration. EvoChecker can also be used at runtime, to drive the efficient reconfiguration of a self-adaptive software system. We evaluate EvoChecker on several variants of three systems from different application domains, and show its effectiveness and applicability.     

Bayesian analysis of empirical software engineering cost models

Many parametric software estimation models have evolved in the last two decades (L.H. Putnam and W. Myers, 1992; C. Jones, 1997; R.M. Park et al., 1992). Almost all of these parametric models have been empirically calibrated to actual data from completed software projects. The most commonly used technique for empirical calibration has been the popular classical multiple regression approach. As discussed in the paper, the multiple regression approach imposes a few assumptions frequently violated by software engineering datasets. The paper illustrates the problems faced by the multiple regression approach during the calibration of one of the popular software engineering cost models, COCOMO II. It describes the use of a pragmatic 10 percent weighted average approach that was used for the first publicly available calibrated version (S. Chulani et al., 1998). It then moves on to show how a more sophisticated Bayesian approach can be used to alleviate some of the problems faced by multiple regression. It compares and contrasts the two empirical approaches, and concludes that the Bayesian approach was better and more robust than the multiple regression approach     

On the dataset shift problem in software engineering prediction models

A core assumption of any prediction model is that test data distribution does not differ from training data distribution. Prediction models used in software engineering are no exception. In reality, this assumption can be violated in many ways resulting in inconsistent and non-transferrable observations across different cases. The goal of this paper is to explain the phenomena of conclusion instability through the dataset shift concept from software effort and fault prediction perspective. Different types of dataset shift are explained with examples from software engineering, and techniques for addressing associated problems are discussed. While dataset shifts in the form of sample selection bias and imbalanced data are well-known in software engineering research, understanding other types is relevant for possible interpretations of the non-transferable results across different sites and studies. Software engineering community should be aware of and account for the dataset shift related issues when evaluating the validity of research outcomes.     

Experimentation with dynamic simulation models in software engineering: planning and reporting guidelines

Simulation-based studies (SBS) have become an interesting investigation approach for Software Engineering (SE). However, the reports on experiments with dynamic simulation models found in the technical literature lack relevant information, hampering the full understanding of the procedures and results reported, as well as their replicability. Apart from the limitations on the length in conferences and journal papers, some of the relevant information seems to be missing due to methodological issues not considered when conducting such studies. This is the case of missing research questions and goals, lack of evidence regarding the dynamic simulation model validity, poorly designed simulation experiments, amongst others. Based on findings from a previous quasi-systematic literature review, we propose a set of reporting guidelines for SBS with dynamic models in the context of SE aiming at providing guidance on which information the report should contain. Furthermore, these guidelines were evolved to support SBS planning by identifying potential threats to simulation study validity and in making recommendations to avoid them, through qualitative analysis and external evaluation. Finally, we conducted different evaluations regarding both the reporting and planning guidelines, apart from using them to support the planning of a SBS as regards software evolution. A set of 33 reporting and planning guidelines for different stages of the simulation lifecycle and focused on the experimentation with dynamic simulation models have been put together. The first assessments point to a comprehensive set of guidelines, supporting a comprehensive preparation and review of the plans and reports from the studies, apart from the planning of a SBS focused on software evolution, potentially reducing the threats to the experimentation with the validity of dynamic simulation models. The 33 guidelines cannot be understood as separate groups for reporting and planning as they overlap in many aspects. The main goal is to use the guidelines to support the planning of a simulation-based study with dynamic models so that experimenters may identify potential threats to validity and produce relevant information for a complete simulation experiment report in advance. Despite their initial contribution to increase the validity of SBS, the reporting and planning of simulation-based experiments with dynamic models still has to be discussed and improved in SE. Therefore, additional assessments of this set of guidelines are needed to strengthen the confidence in their completeness and usefulness.     

On the generation of requirements specifications from software engineering models: A systematic literature review

System and software requirements documents play a crucial role in software engineering in that they must both communicate requirements to clients in an understandable manner and define requirements in precise detail for system developers. The benefits of both lists of textual requirements (usually written in natural language) and software engineering models (usually specified in graphical form) can be brought together by combining the two approaches in the specification of system and software requirements documents. If, moreover, textual requirements are generated from models in an automatic or closely monitored form, the effort of specifying those requirements is reduced and the completeness of the specification and the management of the requirements traceability are improved. This paper presents a systematic review of the literature related to the generation of textual requirements specifications from software engineering models.     

Representing designed artifacts: The languages of engineering design

Summary Design knowledge incorporates information about designed objects and their attributes, as well as about other aspects of the design process. Such information about designed artifacts and any associated design concepts can be represented in several different forms or languages. This paper describes the languages of design, emphasizing particularly the representation of designed objects. Inasmuch as some of these design languages derive from computational styles, and since all are used to develop computational models of design, these languages form a useful backdrop for understanding and furthering the role of computers in engineering design. This paper has been adapted from Chapter 5 of the book, Engineering Design: A Synthesis of Views. The author is grateful to the publisher, Cambridge University Press, for permitting this adaptation.     

Representing the behaviour of software projects using multi-dimensional timelines

ContextThere are few empirical studies in the empirical software engineering research community that describe software projects, at the level of the project, as they progress over time.ObjectiveTo investigate how to coherently represent a large volume of qualitative and quantitative data on a range of project-level attributes as those attributes change over time.MethodDevelop a modelling technique, multi-dimensional timelines (MDTs) and undertake a preliminary appraisal of the technique using examples from a longitudinal case study of a project at IBM Hursley Park.ResultsMDTs can represent project-level attributes as they change over time, provided these attributes, and the empirical data about them, can be located in time (an analytical requirement) and can be represented in terms of the simple geometrical structures of points, lines and planes (a graphical requirement). Changes in attributes are documented at the point in time at which the change occurs. There are a number of ways in which an attribute can be represented on the MDT: as a quantitative time series, as an event, as an event with label containing complex qualitative information, or as a schedule. The MDT technique is currently not capable of representing relationships between different attributes e.g. a causal relationship.ConclusionThe initial appraisal of MDTs is encouraging, but further work is needed on the development of the MDT technique and on its evaluation.     

Toward data mining engineering: A software engineering approach

The number, variety and complexity of projects involving data mining or knowledge discovery in databases activities have increased just lately at such a pace that aspects related to their development process need to be standardized for results to be integrated, reused and interchanged in the future. Data mining projects are quickly becoming engineering projects, and current standard processes, like CRISP-DM, need to be revisited to incorporate this engineering viewpoint. This is the central motivation of this paper that makes the point that experience gained about the software development process over almost 40 years could be reused and integrated to improve data mining processes. Consequently, this paper proposes to reuse ideas and concepts underlying the IEEE Std 1074 and ISO 12207 software engineering model processes to redefine and add to the CRISP-DM process and make it a data mining engineering standard.     

Automated software engineering: supporting understanding

The most important role for automation in software engineering is the support of human understanding. Some aspects of understanding and how it can be supported are discussed.     

Management-aided software engineering

Two veteran software managers examine some of today's management best practices for signs of what might become generally accepted practice in the near future. They present these practices from a small sample of healthy organizations. In addition, they envision how software management might mature over the next few decades to produce a new generation of best practices     

From knowledge based software engineering to knowware based software engineering

The first part of this paper reviews our efforts on knowledge-based software engineering, namely PROMIS, started from 1990s. The key point of PROMIS is to generate applications automatically based on domain knowledge as well as software knowledge. That is featured by separating the development of domain knowledge from the development of software. But in PROMIS, we did not find an appropriate representation for the domain knowledge. Fortunately, in our recent work, we found such a carrier for knowledge modules, i.e. knowware. Knowware is a commercialized form of domain knowledge. This paper briefly introduces the basic definitions of knowware, knowledge middleware and knowware engineering. Three life circle models of knowware engineering and the design of corresponding knowware implementations are given. Finally we discuss application system automatic generation and domain knowledge modeling on the J2EE platform, which combines the techniques of PROMIS, knowware and J2EE, and the development and deployment framework, i.e. PROMIS/KW＊＊.