Carmine Gravino

Design pattern recovery through visual language parsing and source code analysis

In this paper we propose an approach for recovering structural design patterns from object-oriented source code. The recovery process is organized in two phases. In the first phase, the design pattern instances are identified at a coarse-grained level by considering the design structure only and exploiting a parsing technique used for visual language recognition. Then, the identified candidate patterns are validated by a fine-grained source code analysis phase. The recognition process is supported by a tool, namely design pattern recovery environment, which allowed us to assess the retrieval effectiveness of the proposed approach on six public-domain programs and libraries.

Design pattern recovery by visual language parsing

We propose an object oriented (OO) design pattern recovery approach which makes use of a design pattern library, expressed in terms of visual grammars, and based on a visual language parsing technique. We also present a visual environment, which supports the pattern recognition process by automatically retrieving design patterns from imported UML class diagrams. The visual environment has been automatically generated through the VLDesk system, starting from a description of the design pattern grammar.

Effort estimation modeling techniques: a case study for web applications

A reliable effort estimation is crucial for a successful web application development planning. Several approaches exist to address this issue. Among them, the algorithmic approach is one of the most widely used and investigated methods. It is based on suitable effort prediction models which relate the development effort with project characteristics. The size represents one of the most interesting characteristics of software products and several measures can be defined in order to estimate the size of web systems. Moreover, several techniques have been proposed in the literature to build the effort prediction models. Thus, of special interest should be to establish the most effective size measures to be employed in effort prediction models and the most suitable techniques for the model construction. To this aim some empirical studies have been undertaken so far. Since it is widely recognized that several investigations should be performed to verify/confirm empirical results, in the paper we will report on an empirical analysis we have carried out by exploiting data coming from 15 web projects developed by a software company. In particular, for the analysis we have considered two sets of size measures: Length Measures (e.g. number of pages, number of medias, number of client and server side scripts) and Functional Measures (e.g. external input, external output, external query). Moreover, we have employed different techniques, such as Linear Regression, Regression Tree, and Analogy-Based Estimation, in order to determine the one that provides the best prediction.

Assessing the Effectiveness of Sequence Diagrams in the Comprehension of Functional Requirements: Results from a Family of Five Experiments

Modeling is a fundamental activity within the requirements engineering process and concerns the construction of abstract descriptions of requirements that are amenable to interpretation and validation. The choice of a modeling technique is critical whenever it is necessary to discuss the interpretation and validation of requirements. This is particularly true in the case of functional requirements and stakeholders with divergent goals and different backgrounds and experience. This paper presents the results of a family of experiments conducted with students and professionals to investigate whether the comprehension of functional requirements is influenced by the use of dynamic models that are represented by means of the UML sequence diagrams. The family contains five experiments performed in different locations and with 112 participants of different abilities and levels of experience with UML. The results show that sequence diagrams improve the comprehension of the modeled functional requirements in the case of high ability and more experienced participants.

Using tabu search to configure support vector regression for effort estimation

Recent studies have reported that Support Vector Regression (SVR) has the potential as a technique for software development effort estimation. However, its prediction accuracy is heavily influenced by the setting of parameters that needs to be done when employing it. No general guidelines are available to select these parameters, whose choice also depends on the characteristics of the dataset being used. This motivated the work described in (Corazza et al. 2010), extended herein. In order to automatically select suitable SVR parameters we proposed an approach based on the use of the meta-heuristics Tabu Search (TS). We designed TS to search for the parameters of both the support vector algorithm and of the employed kernel function, namely RBF. We empirically assessed the effectiveness of the approach using different types of datasets (single and cross-company datasets, Web and not Web projects) from the PROMISE repository and from the Tukutuku database. A total of 21 datasets were employed to perform a 10-fold or a leave-one-out cross-validation, depending on the size of the dataset. Several benchmarks were taken into account to assess both the effectiveness of TS to set SVR parameters and the prediction accuracy of the proposed approach with respect to widely used effort estimation techniques. The use of TS allowed us to automatically obtain suitable parameters’ choices required to run SVR. Moreover, the combination of TS and SVR significantly outperformed all the other techniques. The proposed approach represents a suitable technique for software development effort estimation.

Improving Behavioral Design Pattern Detection through Model Checking

Recovering design pattern instances in a software system can help maintainers to understand its design and implementation. In this paper we present a fully automated design pattern recovery approach that analyzes the behavior of pattern instances both statically and dynamically. In particular, the proposed approach exploits model checking to statically verify the behavioral aspects of design pattern instances. To this end, we encode the properties defining the correct behavior of a pattern as LTL (Linear Temporal Logic) formulae and the sequence diagram representing the possible interaction traces among the objects involved in the candidate instances as PROMELA specifications. To verify whether the LTL properties are satisfied by the candidates we employ the SPIN model checking tool. The dynamic analysis of the pattern behavior is performed through a code instrumentation and monitoring phase applied on the candidate pattern instances. This phase allows us to obtain actual dynamic data during program execution, which is then used to verify its compliance to the pattern definition. The effectiveness of the proposed approach is shown by presenting and discussing the results obtained on JHotDraw and JRefactory.

Genetic Programming for Effort Estimation: An Analysis of the Impact of Different Fitness Functions

Context: The use of search-based methods has been recently proposed for software development effort estimation and some case studies have been carried out to assess the effectiveness of Genetic Programming (GP). The results reported in the literature showed that GP can provide an estimation accuracy comparable or slightly better than some widely used techniques and encouraged further research to investigate whether varying the fitness function the estimation accuracy can be improved. Aim: Starting from these considerations, in this paper we report on a case study aiming to analyse the role played by some fitness functions for the accuracy of the estimates. Method: We performed a case study based on a publicly available dataset, i.e., Desharnais, by applying a 3-fold cross validation and employing summary measures and statistical tests for the analysis of the results. Moreover, we compared the accuracy of the obtained estimates with those achieved using some widely used estimation methods, namely Case-Based Reasoning (CBR) and Manual Step Wise Regression (MSWR). Results: The obtained results highlight that the fitness function choice significantly affected the estimation accuracy. The results also revealed that GP provided significantly better estimates than CBR and comparable with those of MSWR for the considered dataset.

An experimental comparison of ER and UML class diagrams for data modelling

We present the results of three sets of controlled experiments aimed at analysing whether UML class diagrams are more comprehensible than ER diagrams during data models maintenance. In particular, we considered the support given by the two notations in the comprehension and interpretation of data models, comprehension of the change to perform to meet a change request, and detection of defects contained in a data model. The experiments involved university students with different levels of ability and experience. The results demonstrate that using UML class diagrams subjects achieved better comprehension levels. With regard to the support given by the two notations during maintenance activities the results demonstrate that the two notations give the same support, while in general UML class diagrams provide a better support with respect to ER diagrams during verification activities.

A genetic algorithm to configure support vector machines for predicting fault-prone components

In some studies, Support Vector Machines (SVMs) have been turned out to be promising for predicting fault-prone software components. Nevertheless, the performance of the method depends on the setting of some parameters. To address this issue, we propose the use of a Genetic Algorithm (GA) to search for a suitable configuration of SVMs parameters that allows us to obtain optimal prediction performance. The approach has been assessed carrying out an empirical analysis based on jEdit data from the PROMISE repository. We analyzed both the inter- and the intra-release performance of the proposed method. As benchmarks we exploited SVMs with Grid-search and several other machine learning techniques. The results show that the proposed approach let us to obtain an improvement of the performance with an increasing of the Recall measure without worsening the Precision one. This behavior was especially remarkable for the inter-release use with respect to the other prediction techniques.

A Case Study Using Web Objects and COSMIC for Effort Estimation of Web Applications

Some size measures have been proposed in the literature to be employed in estimating Web application development effort, but to date few empirical studies have been undertaken to validate them and compare their effectiveness. To address this issue we have performed an empirical study by using as size measures COSMIC and Web Objects, with the aim of verifying their effectiveness as indicators of development effort. In particular, we have built two effort estimation models by applying the Ordinary Least-Squares regression and by using a dataset of 15 Web applications developed by an italian software company. The performance of the obtained models have been evaluated by using a dataset of further 4 Web applications developed by the same software company some time after the first 15 Web applications. The results reveal that both Web Objects and COSMIC are good indicators of the development effort.