In order to support the maintenance of an objectoriented software system, the quality of its design must be evaluated using adequate quantification means. In spite of the current extensive use of metrics, if used in isolation metrics are oftentimes too fine grained to quantify comprehensively an investigated design aspect (e.g., distribution of system’s intelligence among classes). To help developers and maintainers detect and localize design problems in a system, we propose a novel mechanism – called detection strategy – for formulating metrics-based rules that capture deviations from good design principles and heuristics. Using detection strategies an engineer can directly localize classes or methods affected by a particular design flaw (e.g., God Class), rather than having to infer the real design problem from a large set of abnormal metric values. We have defined such detection strategies for capturing around ten important flaws of object-oriented design found in the literature and validated the approach experimentally on multiple large-scale case-studies.

In order to support the maintenance of object-oriented software systems, the quality of their design must be evaluated using adequate quantification means. In spite of the current extensive use of metrics, if used in isolation, metrics are oftentimes too fine grained to quantify comprehensively an investigated aspect of the design. To help the software engineer detect and localize design problems, the novel detection strategy mechanism is defined so that deviations from good-design principles and heuristics are quantified in form of metrics-based rules. Using detection strategies an engineer can directly localize classes or methods affected by a particular design flaw (e.g. God Class), rather than having to infer the real design problem from a large set of abnormal metric values. In order to reach the ultimate goal of bridging the gap between qualitative and quantitative statements about design, the dissertation proposes a novel type of quality model, called Factor-Strategy. In contrast to traditional quality models that express the goodness of design in terms of a set of metrics, this novel model relates explicitly the quality of a design to its conformance with a set of essential principles, rules and heuristics, which are quantified using detection strategies. 1

We describe a meta-method for formal method integration [Pai97]. The approach is applied to combining formal methods with other formal and semiformal methods. We discuss the theory behind formal method integration, present two example combinations, and use an integrated method in solving a small problem.

Method integration is the procedure of combining multiple methods to form a new technique. In the context of software engineering, this can involve combining specification techniques, rules and guidelines for design and implementation, and sequences of steps for managing an entire development. In current practice, method integration is often an ad-hoc process, where links between methods are defined on a case-by-case basis. In this dissertation, we examine an approach to formal method integration based on so-called heterogeneous notations: compositions of compatible notations. We set up a basis that can be used to formally define the meaning of compositions of formal and semiformal notations. Then, we examine how this basis can be used in combining methods used for system specification, design, and implementation. We demonst...

The quality of a design has a decisive impact on the quality of a software product; but due to the diversity and complexity of design properties (e.g., coupling, encapsulation), their assessment and correlation with external quality attributes (e.g., maintenance, portability) is hard. In contrast to traditional quality models that express the “goodness ” of design in terms of a set of metrics, the novel Factor-Strategy model proposed by this paper, relates explicitly the quality of a design to its conformance with a set of essential principles, rules and heuristics. This model is based on a novel mechanism, called detection strategy, that raises the abstraction level in dealing with metrics, by allowing to formulate good-design rules and heuristics in a quantifiable manner, and to detect automatically deviations from these rules. This quality model provides a twofold advantage: (i) an easier construction and understanding of the model as quality is put in connection with design principles rather than “raw numbers”; and (ii) a direct identification of the real causes of quality flaws. We have validated the approach through a comparative analysis involving two versions of a industrial software system.

Over the years software engineering researchers have suggested numerous techniques for estimating development effort. These techniques have been classified mainly as algorithmic, machine learning and expert judgement. Several studies have compared the prediction accuracy of those techniques, with emphasis placed on linear regression, stepwise regression, and Case-based Reasoning (CBR). To date no converging results have been obtained and we believe they may be influenced by the use of the same CBR configuration. The objective of this paper is twofold. First, to describe the application of case-based reasoning for estimating the effort for developing Web hypermedia applications. Second, comparing the prediction accuracy of different CBR configurations, using two Web hypermedia datasets. Results show that for both datasets the best estimations were obtained with weighted Euclidean distance, using either one analogy (dataset 1) or 3 analogies (dataset 2). We suggest therefore that case-based reasoning is a candidate technique for effort estimation and, with the aid of an automated environment, can be applied to Web hypermedia development effort prediction. Categories and Subject Descriptors D.2 [Software Engineering]: D.2.8 Metrics – complexity

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AbstractÐSoftware size is a fundamental product measure that can be used for assessment, prediction, and improvement purposes. However, existing software size measures, such as Function Points, do not address the underlying problem complexity of software systems adequately. This can result in disproportional measures of software size for different types of systems. We propose a Vector Size Measure (VSM) that incorporates both functionality and problem complexity in a balanced and orthogonal manner. VSM is used as the input to a Vector Prediction Model (VPM) which can be used to estimate development effort early in the software life cycle. We theoretically validate the approach against a formal framework. We also empirically validate the approach with a pilot study. The results indicate that the approach provides a mechanism to measure the size of software systems, classify software systems, and estimate development effort early in the software life cycle to within +/-20 percent across a range of application types.

It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process. Our minimum data requirement is a single known point. The technique is supported by a software tool known as DataSalvage. We show for two industrial projects how our approach based upon expert judgement adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then descri...

Abstract—Understanding how to implement software process improvement (SPI) successfully is arguably the most challenging issue facing the SPI field today. The SPI literature contains many case studies of successful companies and descriptions of their SPI programs. However, the research efforts to date are limited and inconclusive and without adequate theoretical and psychometric justification. This paper extends and integrates models from prior research by performing an empirical investigation of the key factors for success in SPI. A quantitative survey of 120 software organizations was designed to test the conceptual model and hypotheses of the study. The results indicate that success depends critically on six organizational factors, which explained more than 50 percent of the variance in the outcome variable. The main contribution of the paper is to increase the understanding of the influence of organizational issues by empirically showing that they are at least as important as technology for succeeding with SPI and, thus, to provide researchers and practitioners with important new insights regarding the critical factors of success in SPI.