Multiple-parameter coupling metrics for layered component-based software

Coupling represents the degree of interdependence between two software components. Understanding software dependency is directly related to improving software understandability, maintainability, and reusability. In this paper, we analyze the difference between component coupling and component dependency, introduce a two-parameter component coupling metric and a three-parameter component dependency metric. An important parameter in both these metrics is coupling distance, which represents the relevance of two coupled components. These metrics are applicable to layered component-based software. These metrics can be used to represent the dependencies induced by all types of software coupling. We show how to determine coupling and dependency of all scales of software components using these metrics. These metrics are then applied to Apache HTTP, an open-source web server. The study shows that coupling distance is related to the number of modifications of a component, which is an important indicator of component fault rate, stability and subsequently, component complexity.

A proposal for a set of attributes relevant for Web portal data quality

Data Quality is a critical issue in today’s interconnected society. Advances in technology are making the use of the Internet an ever-growing phenomenon and we are witnessing the creation of a great variety of applications such as Web Portals. These applications are important data sources and/or means of accessing information which many people use to make decisions or to carry out tasks. Quality is a very important factor in any software product and also in data. As quality is a wide concept, quality models are usually used to assess the quality of a software product. From the software point of view there is a widely accepted standard proposed by ISO/IEC (the ISO/IEC 9126) which proposes a quality model for software products. However, until now a similar proposal for data quality has not existed. Although we have found some proposals of data quality models, some of them working as “de facto” standards, none of them focus specifically on web portal data quality and the user’s perspective. In this paper, we propose a set of 33 attributes which are relevant for portal data quality. These have been obtained from a revision of literature and a validation process carried out by means of a survey. Although these attributes do not conform to a usable model, we think that it might be considered as a good starting point for constructing one.

Improvement of causal analysis using multivariate statistical process control

Statistical process control (SPC) is a conventional means of monitoring software processes and detecting related problems, where the causes of detected problems can be identified using causal analysis. Determining the actual causes of reported problems requires significant effort due to the large number of possible causes. This study presents an approach to detect problems and identify the causes of problems using multivariate SPC. This proposed method can be applied to monitor multiple measures of software process simultaneously. The measures which are detected as the major impacts to the out-of-control signals can be used to identify the causes where the partial least squares (PLS) and statistical hypothesis testing are utilized to validate the identified causes of problems in this study. The main advantage of the proposed approach is that the correlated indices can be monitored simultaneously to facilitate the causal analysis of a software process.

Motivating developer performance to improve project outcomes in a high maturity organization

In this paper we discuss the impact software developer performance has on project outcomes. Project performance remains unreliable in the software industry with many compromised software systems reported in the press. We investigate the impact that developer performance has on aspects of project success and explore how developer performance is motivated. We present interview, focus group and questionnaire data collected from a team of developers working in a software organization that has been assessed at CMM level 5. Our main findings are that developers value technical skills in their colleagues, but appreciate these especially when supplemented with good human skills. Software developers with a proactive, flexible, adaptable approach who are prepared to share knowledge and follow good practice are said to be the best developers. Motivators for these developers are pay and benefits, recognition and opportunities for achievement in their work. Overall, we found that technical competence, interpersonal skills and adherence to good practices are thought to have the biggest impact on software project success.

On detecting faults for Boolean expressions

Fault based testing aims at detecting hypothesized faults based on specifications or program source. There are some fault based techniques for testing Boolean expressions which are commonly used to model conditions in specifications as well as logical decisions in program source. The MUMCUT strategy has been proposed to generate test cases from Boolean expressions. Moreover, it detects eight common types of hypothesized faults provided that the original expression is in irredundant disjunctive normal form, IDNF. Software practitioners are more likely to write the conditions and logical decisions in general form rather than IDNF. Hence, it is interesting to investigate the fault detecting capability of the MUMCUT strategy with respect to general form Boolean expressions. In this article, we perform empirical studies to investigate the fault detection capability of the MUMCUT strategy with respect to general form Boolean expressions as well as mutated expressions. A mutated expression can be obtained from the original given Boolean expression by making a syntactic change based on a particular type of fault.

Evaluation of preliminary data analysis framework in software cost estimation based on ISBSG R9 Data

Previous research has argued that preliminary data analysis is necessary for software cost estimation. In this paper, a framework for such analysis is applied to a substantial corpus of historical project data (ISBSG R9 data), selected without explicit bias. The consequent analysis yields sets of dominant variables, which are then used to construct project effort estimation models. Performance of the predictors on the raw variables and the extracted sets of variables is then measured in terms of Mean Magnitude of Relative Error (MMRE), Median of Magnitude of Relative Error (MdMRE) and prediction at levels 0.05, 0.1, and 0.25. The results from the comparative evaluation suggest that more accurate prediction models can be constructed for the selected prediction techniques. The framework processed predictor variables are statistically significant, at the 95% confidence level for both parametric techniques and one non-parametric technique. The results are also compared with the latest published results obtained by other research based on the same data set. The comparison indicates that, the models constructed using framework processed data are generally more accurate.

Software quality analysis by combining multiple projects and learners

When building software quality models, the approach often consists of training data mining learners on a single fit dataset. Typically, this fit dataset contains software metrics collected during a past release of the software project that we want to predict the quality of. In order to improve the predictive accuracy of such quality models, it is common practice to combine the predictive results of multiple learners to take advantage of their respective biases. Although multi-learner classifiers have been proven to be successful in some cases, the improvement is not always significant because the information in the fit dataset sometimes can be insufficient. We present an innovative method to build software quality models using majority voting to combine the predictions of multiple learners induced on multiple training datasets. To our knowledge, no previous study in software quality has attempted to take advantage of multiple software project data repositories which are generally spread across the organization. In a large scale empirical study involving seven real-world datasets and seventeen learners, we show that, on average, combining the predictions of one learner trained on multiple datasets significantly improves the predictive performance compared to one learner induced on a single fit dataset. We also demonstrate empirically that combining multiple learners trained on a single training dataset does not significantly improve the average predictive accuracy compared to the use of a single learner induced on a single fit dataset.

An exploratory study of the effect of aspect-oriented programming on maintainability

In this paper we describe an exploratory assessment of the effect of aspect-oriented programming on software maintainability. An experiment was conducted in which 11 software professionals were asked to carry out maintenance tasks on one of two programs. The first program was written in Java and the second in AspectJ. Both programs implement a shopping system according to the same set of requirements. A number of statistical hypotheses were tested. The results did seem to suggest a slight advantage for the subjects using the object-oriented system since in general it took the subjects less time to answer the questions on this system. Also, both systems appeared to be equally difficult to modify. However, the results did not show a statistically significant influence of aspect-oriented programming at the 5% level. We are aware that the results of this single small study cannot be generalized. We conclude that more empirical research is necessary in this area to identify the benefits of aspect-oriented programming and we hope that this paper will encourage such research.

An analysis of software quality management at AWE plc.

A detailed question set is required to test and measure the true extent that a software quality management system is adopted and implemented across a large company like Atomic Weapons Establishment (AWE) plc. The analysis of the gathered data reveals specific topics of weakness that can also reflect the cultural acceptance or resistance that management groups have towards the adoption of quality systems. Having identified detailed problems and barriers, effective strategies and programmes can be deployed to improve the level of implementation and, therefore, the effectiveness of a software quality management system. This paper presents the question set used and the subsequent results obtained from the implementation assessment for 55 software systems at AWE plc. The data is collated into management groups and the associated cultures discussed. The topics of weakness are highlighted together with the very specific actions that are least undertaken. A range of improvement actions is also presented.

Quantitatively measuring object-oriented couplings

One key to several quality factors of software is the way components are connected. Software coupling can be used to estimate a number of quality factors, including maintainability, complexity, and reliability. Object-oriented languages are designed to reduce the number of dependencies among classes, which encourages separation of concerns and should reduce the amount of coupling. At the same time, the object-oriented language features change the way the connections are made, how they must be analyzed, and how they are measured. This paper discusses software couplings based on object-oriented relationships between classes, specifically focusing on types of couplings that are not available until after the implementation is completed, and presents a static analysis tool that measures couplings among classes in Java packages. Data from evaluating the tool on several open-source projects are provided. The coupling measurement is based on source code, which has the advantage of being quantitative and more precise than previous measures, but the disadvantage of not being available before implementation, and thus not useful for some predictive efforts.

Imputation techniques for multivariate missingness in software measurement data

The problem of missing values in software measurement data used in empirical analysis has led to the proposal of numerous potential solutions. Imputation procedures, for example, have been proposed to ‘fill-in’ the missing values with plausible alternatives. We present a comprehensive study of imputation techniques using real-world software measurement datasets. Two different datasets with dramatically different properties were utilized in this study, with the injection of missing values according to three different missingness mechanisms (MCAR, MAR, and NI). We consider the occurrence of missing values in multiple attributes, and compare three procedures, Bayesian multiple imputation, k Nearest Neighbor imputation, and Mean imputation. We also examine the relationship between noise in the dataset and the performance of the imputation techniques, which has not been addressed previously. Our comprehensive experiments demonstrate conclusively that Bayesian multiple imputation is an extremely effective imputation technique.

Building measure-based prediction models for UML class diagram maintainability

The usefulness of measures for the analysis and design of object oriented (OO) software is increasingly being recognized in the field of software engineering research. In particular, recognition of the need for early indicators of external quality attributes is increasing. We investigate through experimentation whether a collection of UML class diagram measures could be good predictors of two main subcharacteristics of the maintainability of class diagrams: understandability and modifiability. Results obtained from a controlled experiment and a replica support the idea that useful prediction models for class diagrams understandability and modifiability can be built on the basis of early measures, in particular, measures that capture structural complexity through associations and generalizations. Moreover, these measures seem to be correlated with the subjective perception of the subjects about the complexity of the diagrams. This fact shows, to some extent, that the objective measures capture the same aspects as the subjective ones. However, despite our encouraging findings, further empirical studies, especially using data taken from real projects performed in industrial settings, are needed. Such further study will yield a comprehensive body of knowledge and experience about building prediction models for understandability and modifiability.

Simulation-based analysis of UML statechart diagrams: methods and case studies

We present a comprehensive unified modeling language (UML) statechart diagram analysis framework. This framework allows one to progressively perform different analysis operations to analyze UML statechart diagrams at different levels of model complexity. The analysis operations supported by the framework are based on analyzing Petri net models converted from UML statechart diagrams using a previously proposed transformation approach. After introducing the general framework, the paper emphasizes two simulation-based analysis operations from the framework: direct MSC inspection, which provides a visual representation of system behavior described by statechart diagrams; and a pattern-based trace query technique, which can be used to define and query system properties. Two case-study examples are presented with different emphasis. The gas station example is a simple multi-object system used to demonstrate both the visual and query-based analysis operations. The early warning system example uses only one object, but features composite states and includes analysis specifically aimed at one composite state feature, history states.

Supporting high interoperability of components by adopting an agent-based approach

While integrating components into systems, we will be confronted with problems concerned with the interoperability of components due to the interaction mismatches at multiple levels, such as interaction behaviors between components and features imposed by architectural styles. In this paper, we studied the interoperability of components and explored the approach to supporting high interoperability of components involved in mismatching interactions. First, we formalized components involved in different architectural styles in the pi-calculus. Next, we studied the formal foundation of the interoperability of components for reasoning about the conditions under which two heterogeneous components are possible to interoperate and interconnect together properly. Then, we described a wrapper-based solution for integrating components into systems that impose mismatching assumptions about usage of the components. In the end, we presented an agent-based implementation for the solution, in which agents are used to wrap components and can automatically resolve multiple levels of interaction mismatches between components. We also gave a simple example to illustrate our approach.

An evolutionary cultural-change approach to successful software process improvement

With a plethora of models, systems and standards to choose for a basis of software process improvement, decisions on which to adopt may depend on a number of factors. This paper presents an evolutionary and extremely cost effective approach to implementing a software quality system that requires minimum resource and little disruption to programme delivery. The method presented, achieved a 40% improvement in the level of implementation of the AWE plc software quality management system over a 5-year period. A critical success factor is the treatment of the users’ of the defined software quality system as customers, understanding their concerns and problems, and being responsive to them. The importance of a well designed system is highlighted together with the essential and extensive consultation process required to gain buy-in and lay the foundation for cultural change. This was supported with a helpful programme of facilitated self-assessment and sustained by a closely aligned training scheme. As a consequence some of the cultural elements were changed from one of thoughtless “tick-in-the-box” compliance to one of true understanding of the system requirements, true quality implementation, and subsequent added value.

A comprehensive quality model for service-oriented systems

In a service-oriented system, a quality (or Quality of Service) model is used (i) by service requesters to specify the expected quality levels of service delivery; (ii) by service providers to advertise quality levels that their services achieve; and (iii) by service composers when selecting among alternative services those that are to participate in a service composition. Expressive quality models are needed to let requesters specify quality expectations, providers advertise service qualities, and composers finely compare alternative services. Having observed many similarities between various quality models proposed in the literature, we review these and integrate them into a single quality model, called QVDP. We highlight the need for integration of priority and dependency information within any quality model for services and propose precise submodels for doing so. Our intention is for the proposed model to serve as a reference point for further developments in quality models for service-oriented systems. To this aim, we extend the part of the UML metamodel specialized for Quality of Service with QVDP concepts unavailable in UML.

Comparing distributed and face-to-face meetings for software architecture evaluation: A controlled experiment

Scenario-based methods for evaluating software architecture require a large number of stakeholders to be collocated for evaluation meetings. Collocating stakeholders is often an expensive exercise. To reduce expense, we have proposed a framework for supporting software architecture evaluation process using groupware systems. This paper presents a controlled experiment that we conducted to assess the effectiveness of one of the key activities, developing scenario profiles, of the proposed groupware-supported process of evaluating software architecture. We used a cross-over experiment involving 32 teams of three 3rd and 4th year undergraduate students. We found that the quality of scenario profiles developed by distributed teams using a groupware tool were significantly better than the quality of scenario profiles developed by face-to-face teams (p < 0.001). However, questionnaires indicated that most participants preferred the face-to-face arrangement (82%) and 60% thought the distributed meetings were less efficient. We conclude that distributed meetings for developing scenario profiles are extremely effective but that tool support must be of a high standard or participants will not find distributed meetings acceptable.

Face recognition using an NNSRM classifier in LDA subspace

NNSRM is an implementation of the structural risk minimization (SRM) principle using the nearest neighbor (NN) rule, and linear discriminant analysis (LDA) is a dimension-reducing method, which is usually used in classifications. This paper combines the two methods for face recognition. We first project the face images into a PCA subspace, then project the results into a much lower-dimensional LDA subspace, and then use an NNSRM classifier to recognize them in the LDA subspace. Experimental results demonstrate that the combined method can achieve a better performance than NN by selecting different distances and a comparable performance with SVM but costing less computational time.

An experimental investigation of personality types impact on pair effectiveness in pair programming

In this paper, pair programming is empirically investigated from the perspective of developer personalities and temperaments and how they affect pair effectiveness. A controlled experiment was conducted to investigate the impact of developer personalities and temperaments on communication, pair performance and pair viability-collaboration. The experiment involved 70 undergraduate students and the objective was to compare pairs of heterogeneous developer personalities and temperaments with pairs of homogeneous personalities and temperaments, in terms of pair effectiveness. Pair effectiveness is expressed in terms of pair performance, measured by communication, velocity, design correctness and passed acceptance tests, and pair collaboration-viability measured by developers’ satisfaction, knowledge acquisition and participation. The results have shown that there is important difference between the two groups, indicating better communication, pair performance and pair collaboration-viability for the pairs with heterogeneous personalities and temperaments. In order to provide an objective assessment of the differences between the two groups of pairs, a number of statistical tests and stepwise Discriminant Analysis were used.