An empirical investigation on the reusability of design patterns and software packages

Nowadays open-source software communities are thriving. Successful open-source projects are competitive and the amount of source code that is freely available offers great reuse opportunities to software developers. Thus, it is expected that several requirements can be implemented based on open source software reuse. Additionally, design patterns, i.e. well-known solution to common design problems, are introduced as elements of reuse. This study attempts to empirically investigate the reusability of design patterns, classes and software packages. Thus, the results can help developers to identify the most beneficial starting points for white box reuse, which is quite popular among open source communities. In order to achieve this goal we conducted a case study on one hundred (100) open source projects. More specifically, we identified 27,461 classes that participate in design patterns and compared the reusability of each of these classes with the reusability of the pattern and the package that this class belongs to. In more than 40% of the cases investigated, design pattern based class selection, offers the most reusable starting point for white-box reuse. However there are several cases when package based selection might be preferable. The results suggest that each pattern has different level of reusability.     

Facilitating software extension with design patterns and Aspect-Oriented Programming

Software products, especially large applications, need to continuously evolve, in order to adapt to the changing environment and updated requirements. With both the producer and the customer unwilling to replace the existing application with a completely new one, adoption of design constructs and techniques which facilitate the application extension is a major design issue. In the current work we investigate the behavior of an object-oriented software application at a specific extension scenario, following three implementation alternatives with regards to a certain design problem relevant to the extension. The first alternative follows a simplistic solution, the second makes use of a design pattern and the third applies Aspect-Oriented Programming techniques to implement the same pattern. An assessment of the three alternatives is attempted, both on a qualitative and a quantitative level, by identifying the additional design implications needed to perform the extension and evaluating the effect of the extension on several quality attributes of the application.     

Human and program factors affecting the maintenance of programs with deployed design patterns

Context

Practitioners may use design patterns to organize program code. Various empirical studies have investigated the effects of pattern deployment and work experience on the effectiveness and efficiency of program maintenance. However, results from these studies are not all consistent. Moreover, these studies have not considered some interesting factors, such as a maintainer’s prior exposure to the program under maintenance.

Objective

This paper aims at identifying what factors may contribute to the productivity of maintainers in the context of making correct software changes when they work on programs with deployed design patterns.

Method

We performed an empirical study involving 118 human subjects with three change tasks on a medium-sized program to explore the possible effects of a suite of six human and program factors on the productivity of maintainers, measured by the time taken to produce a correctly revised program in a course-based setting. The factors we studied include the deployment of design patterns and the presence of pattern-unaware solutions, as well as the maintainer’s prior exposure to design patterns, the subject program and the programming language, and prior work experience.

Results

Among the factors under examination, we find that the deployment of design patterns, prior exposure to the program and the presence of pattern-unaware solutions are strongly correlated with the time taken to correctly complete maintenance tasks. We also report some interesting observations from the experiment.

Conclusion

A new factor, namely, the presence of pattern-unaware solutions, contributes to the efficient completion of maintenance tasks of programs with deployed design patterns. Moreover, we conclude from the study that neither prior exposure to design patterns nor prior exposure to the programming language is supported by sufficient evidences to be significant factors, whereas the subjects’ exposure to the program under maintenance is notably more important.     

A methodology to assess the impact of design patterns on software quality

Context

Software quality is considered to be one of the most important concerns of software production teams. Additionally, design patterns are documented solutions to common design problems that are expected to enhance software quality. Until now, the results on the effect of design patterns on software quality are controversial.

Aims

This study aims to propose a methodology for comparing design patterns to alternative designs with an analytical method. Additionally, the study illustrates the methodology by comparing three design patterns with two alternative solutions, with respect to several quality attributes.

Method

The paper introduces a theoretical/analytical methodology to compare sets of “canonical” solutions to design problems. The study is theoretical in the sense that the solutions are disconnected from real systems, even though they stem from concrete problems. The study is analytical in the sense that the solutions are compared based on their possible numbers of classes and on equations representing the values of the various structural quality attributes in function of these numbers of classes. The exploratory designs have been produced by studying the literature, by investigating open-source projects and by using design patterns. In addition to that, we have created a tool that helps practitioners in choosing the optimal design solution, according to their special needs.

Results

The results of our research suggest that the decision of applying a design pattern is usually a trade-off, because patterns are not universally good or bad. Patterns typically improve certain aspects of software quality, while they might weaken some other.

Conclusions

Concluding the proposed methodology is applicable for comparing patterns and alternative designs, and highlights existing threshold that when surpassed the design pattern is getting more or less beneficial than the alternative design. More specifically, the identification of such thresholds can become very useful for decision making during system design and refactoring.     

An empirical study on the implementation and evaluation of a goal-driven software development risk management model

ContextBuilding a quality software product in the shortest possible time to satisfy the global market demand gives an enterprise a competitive advantage. However, uncertainties and risks exist at every stage of a software development project. These can have an extremely high influence on the success of the final software product. Early risk management practice is effective to manage such risks and contributes effectively towards the project success.ObjectiveDespite risk management approaches, a detailed guideline that explains where to integrate risk management activities into the project is still missing. Little effort has been directed towards the evaluation of the overall impact of a risk management method. We present a Goal-driven Software Development Risk Management Model (GSRM) and its explicit integration into the requirements engineering phase and an empirical investigation result of applying GSRM into a project.MethodWe combine the case study method with action research so that the results from the case study directly contribute to manage the studied project risks and to identify ways to improve the proposed methodology. The data is collected from multiple sources and analysed both in a qualitative and quantitative way.ResultsWhen risk factors are beyond the control of the project manager and project environment, it is difficult to control these risks. The project scope affects all the dimensions of risk. GSRM is a reasonable risk management method that can be employed in an industrial context. The study results have been compared against other study results in order to generalise findings and identify contextual factors.ConclusionA formal early stage risk management practice provides early warning related to the problems that exists in a project, and it contributes to the overall project success. It is not necessary to always consider budget and schedule constraints as top priority. There exist issues such as requirements, change management, and user satisfaction which can influence these constraints.     

Selection of third party software in Off-The-Shelf-based software development—An interview study with industrial practitioners

The success of software development using third party components highly depends on the ability to select a suitable component for the intended application. The evidence shows that there is limited knowledge about current industrial OTS selection practices. As a result, there is often a gap between theory and practice, and the proposed methods for supporting selection are rarely adopted in the industrial practice. This paper's goal is to investigate the actual industrial practice of component selection in order to provide an initial empirical basis that allows the reconciliation of research and industrial endeavors. The study consisted of semi-structured interviews with 23 employees from 20 different software-intensive companies that mostly develop web information system applications. It provides qualitative information that help to further understand these practices, and emphasize some aspects that have been overlooked by researchers. For instance, although the literature claims that component repositories are important for locating reusable components; these are hardly used in industrial practice. Instead, other resources that have not received considerable attention are used with this aim. Practices and potential market niches for software-intensive companies have been also identified. The results are valuable from both the research and the industrial perspectives as they provide a basis for formulating well-substantiated hypotheses and more effective improvement strategies.     

An empirical study on software defect prediction with a simplified metric set

ContextSoftware defect prediction plays a crucial role in estimating the most defect-prone components of software, and a large number of studies have pursued improving prediction accuracy within a project or across projects. However, the rules for making an appropriate decision between within- and cross-project defect prediction when available historical data are insufficient remain unclear.ObjectiveThe objective of this work is to validate the feasibility of the predictor built with a simplified metric set for software defect prediction in different scenarios, and to investigate practical guidelines for the choice of training data, classifier and metric subset of a given project.MethodFirst, based on six typical classifiers, three types of predictors using the size of software metric set were constructed in three scenarios. Then, we validated the acceptable performance of the predictor based on Top-k metrics in terms of statistical methods. Finally, we attempted to minimize the Top-k metric subset by removing redundant metrics, and we tested the stability of such a minimum metric subset with one-way ANOVA tests.ResultsThe study has been conducted on 34 releases of 10 open-source projects available at the PROMISE repository. The findings indicate that the predictors built with either Top-k metrics or the minimum metric subset can provide an acceptable result compared with benchmark predictors. The guideline for choosing a suitable simplified metric set in different scenarios is presented in Table 12.ConclusionThe experimental results indicate that (1) the choice of training data for defect prediction should depend on the specific requirement of accuracy; (2) the predictor built with a simplified metric set works well and is very useful in case limited resources are supplied; (3) simple classifiers (e.g., Naïve Bayes) also tend to perform well when using a simplified metric set for defect prediction; and (4) in several cases, the minimum metric subset can be identified to facilitate the procedure of general defect prediction with acceptable loss of prediction precision in practice.     

Successful IT application architecture design: an empirical study

The technical architecture of an IT application is a critical determinant of its successful development. Complementing the software engineering viewpoint, this paper adopts a broader perspective to the design of IT applications and subsequent project success. It reports on a recent empirical study of factors that influence the design of IT application architectures. It identifies five influencing factors that are significantly associated with variation in the time-budget performance of the subsequent development project. Further, it finds that the projects can be classified according to these five attributes into four types that exhibit clear performance differences. The paper infers recommendations for good practice in IT application architecture design, contributes a detailed instrument for research into the area, compares with findings from software engineering, and raises issues for further study.     

An empirical study of a reverse engineering method for the aggregation relationship based on operation propagation

One of the major obstacles in reverse engineering legacy object-oriented systems is the identification of aggregation relationships. An aggregation relationship, also called whole–part relationship, is a form of association relationship where an object is considered as a part of another object. This characteristic is mostly of semantic nature; therefore, it is difficult to distinguish aggregation from association relationships by implementation mechanism. Most reverse engineering methods for aggregation relationships are based on the lifetime dependence of an object on another object since many implementations of aggregation relationships result in such dependence. However, research literature shows that lifetime dependence is not really a primary property of the aggregation relationships. A reverse engineering approach is proposed on the basis of a primary characteristic for aggregation relationship—propagation of operations. To compare the propagation-based method with the lifetime-based method, we apply both methods to ten class libraries, collect their output, and perform statistical analysis to determine the effectiveness of the two methods. The analysis results show that the propagation-based method performs significantly better than the lifetime-based method and by combining both methods simultaneously the complete aggregation relationships can be uncovered for the class libraries in our experiment.

On the use of software design models in software development practice: An empirical investigation

Research into software design models in general, and into the UML in particular, focuses on answering the question how design models are used, completely ignoring the question if they are used. There is an assumption in the literature that the UML is the de facto standard, and that use of design models has had a profound and substantial effect on how software is designed by virtue of models giving the ability to do model-checking, code generation, or automated test generation. However for this assumption to be true, there has to be significant use of design models in practice by developers.This paper presents the results of a survey summarizing the answers of 3785 developers answering the simple question on the extent to which design models are used before coding. We relate their use of models with (i) total years of programming experience, (ii) open or closed development, (iii) educational level, (iv) programming language used, and (v) development type.The answer to our question was that design models are not used very extensively in industry, and where they are used, the use is informal and without tool support, and the notation is often not UML. The use of models decreased with an increase in experience and increased with higher level of qualification. Overall we found that models are used primarily as a communication and collaboration mechanism where there is a need to solve problems and/or get a joint understanding of the overall design in a group. We also conclude that models are seldom updated after initially created and are usually drawn on a whiteboard or on paper.     

An exploratory study of architectural effects on requirements decisions

The question of the “manner in which an existing software architecture affects requirements decision-making” is considered important in the research community; however, to our knowledge, this issue has not been scientifically explored. We do not know, for example, the characteristics of such architectural effects. This paper describes an exploratory study on this question. Specific types of architectural effects on requirements decisions are identified, as are different aspects of the architecture together with the extent of their effects. This paper gives quantitative measures and qualitative interpretation of the findings. The understanding gained from this study has several implications in the areas of: project planning and risk management, requirements engineering (RE) and software architecture (SA) technology, architecture evolution, tighter integration of RE and SA processes, and middleware in architectures. Furthermore, we describe several new hypotheses that have emerged from this study, that provide grounds for future empirical work. This study involved six RE teams (of university students), whose task was to elicit new requirements for upgrading a pre-existing banking software infrastructure. The data collected was based on a new meta-model for requirements decisions, which is a bi-product of this study.     

An empirical study of the effect of complexity, platform, and program type on software development effort of business applications

Several popular cost estimation models like COCOMO and function points use adjustment variables, such as software complexity and platform, to modify original estimates and arrive at final estimates. Using data on 666 programs from 15 software projects, this study empirically tests a research model that studies the influence of three adjustment variables—software complexity, computer platform, and program type (batch or online programs) on software effort. The results confirm that all the three adjustment variables have a significant effect on effort. Further, multiple comparison of means also points to two other results for the data examined. Batch programs involve significantly higher software effort than online programs. Programs rated as complex have significantly higher effort than programs rated as average.     

An empirical study of the impact of team size on software development effort

In this paper, we investigate the impact of team size on the software development effort. Using field data of over 200 software projects from various industries, we empirically test the impact of team size and other variables—such as software size in function points, ICASE tool and programming language type—on software development effort. Our results indicate that software size in function points significantly impacts the software development effort. The two-way interactions between function points and use of ICASE tool, and function points and language type are significant as well. Additionally, the interactions between team size and programming language type, and team size and use of ICASE tool were all significant.

An instrument for the measurement of customer perceptions of quality management in the software industry: An empirical study in India

Most of the available literature on quality management is based on management’s perception; few studies examine critical issues of quality management from the customer’s perspective, especially in the software industry. In order to gain an insight into what customers expect from a product/service, an analysis of quality management from customer’s point of view is essential. Such an understanding would help the managers to adopt strategies that can enhance the satisfaction level of their customers. The present study highlights the critical factors of quality management in the software industry from the customer’s perspective. Six critical factors are identified: and an instrument, comprising these factors, is developed and validated so as to measure the customer’s perception of quality management in the software industry. George Issac is an Assistant Professor in Mar Athanasius College of Engineering, Kothamangalam, India. He holds a B.Sc (Engg.) degree in Mechanical Engineering from Kerala University, and M.Tech and Ph.D degrees in Management from IIT Madras. His research areas are TQM and Organizational Behavior. He has published articles in the area of quality management in journals such as Total Quality Management and Business Excellence, and Quality Management Journal of American Society for Quality. Chandrasekharan Rajendran is a Professor of Operations Management in the Indian Institute of Technology Madras. His research interests are in TQM, Scheduling and Simulation. He has published several articles in international journals. He has publications in the area of TQM in journals such as Total Quality Management, International Journal of Production Research, International Journal of Service Industry Management, International Journal of Bank Marketing, and Journal of Services Marketing. He serves as referee for many journals. He is a recipient of the Alexander von Humboldt Fellowship of Germany. R.N. Anantharaman is a Professor of Management in the Indian Institute of Technology, Madras. His research interests are in TQM, Organizational Behavior and Industrial Psychology. He has published several articles in international journals. He has published research articles in the area of TQM, HRM Practices, Change Management, etc., in Journals such as Total Quality Management, International Journal of Production Research, International Journal of Bank Marketing, International Journal of Service Industry Management, Journal of Services Marketing, International Journal of Human Resource Management, and Journal of Transnational Management Development.     

Impacts of courseware and student characteristics on learning achievement: results of an empirical study

Abstract Literature reviews revealed that not enough is known about specific software design and use features which contribute to learning effectiveness. Moreover, most research on these topics is carried out only in universities. Therefore, it was decided to conduct an empirical study with students of secondary schools, level two in Switzerland. Five classes and three pieces of courseware material were included in the study to investigate whether and how courseware and student characteristics have an impact upon the learning effectiveness.     

Linking employees’ e-learning system use to their overall job outcomes: An empirical study based on the IS success model

e-Learning systems are increasingly being adopted by modern enterprises, because of their cost-down effect. However, the extant literature provides little insight into their beneficial consequences. It is expected that a transfer of acquired knowledge, skills and/or work attitudes to jobs will occur through e-learning systems use. This makes e-learning systems a useful tool for improving job outcomes, although this is not well documented. The IS success model, which captures both the human and technological elements of information systems, provides a theoretical basis for linking system use to system task. The current study then examines the relation between e-learning systems use and overall job outcomes based on the model. The data collected from one hundred and ninety-three e-learning system users were analyzed with partial least square (PLS). The results indicate that e-learning systems are perceived as useful and satisfying by employees, and employees’ e-learning systems use is significantly associated with overall job outcomes. In practice, these results provide a basis for establishing a link between an organization’s investment in e-learning and human capital management. They also lend empirical support to the IS success model.