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This document is a guest editorial for a special issue of the journal "Programming & Computer Software" titled "Recent Trends on Advanced Computing: The Converging Technologies." The issue features research and practical implementation results from researchers and industry experts in computer science, engineering, and technology. The papers cover a range of topics including microservices, software quality, user engagement on social media, non-functional requirements, medical software architecture, fallacies in political speeches, intelligent learning environments, and more. The guest editors for this special issue are Prof. Dr. Andrei Tchernykh, Prof. Reyes Juárez Ramírez, Dr. Esteban Mocskos, and Prof. Sergio Nesmachnow. [Extracted from the article]

Engineers use software defect prediction (SDP) to locate vulnerable areas of software. Recently, statement-level SDP has attracted the attention of researchers due to its ability to localize faulty code areas. This paper proposes DP-Tramo, a new model dedicated to improving the state-of-the-art statement-level SDP. We use Clang to extract abstract syntax trees from source code and extract 32 statement-level metrics as static features for each sentence. Then we feed static features and token sequences as inputs to our improved R-Transformer to learn the syntactic and semantic features of the code. Furthermore, we use label smoothing and weighted loss to improve the performance of DP-Tramo. To evaluate DP-Tramo, we perform a 10-fold cross-validation on 119,989 C/C++ programs selected from Code4Bench. Experimental results show that DP-Tramo can classify the dataset with an average performance of 0.949, 0.602, 0.734 and 0.737 regarding the recall, precision, accuracy and F1-measure, respectively. DP-Tramo outperforms the baseline method on F1-measure by 1.2% while maintaining a high recall rate. [ABSTRACT FROM AUTHOR]

Models are the key tools humans use to manage complexity in description, development, and analysis. This applies to all scientific and engineering disciplines and in particular to the development of software and data-intensive systems. However, different methods and terminologies have become established in the individual disciplines, even in the sub-fields of Informatics, which raises the need for a comprehensive and cross-sectional analysis of the past, present, and future of modeling research. This paper aims to shed some light on how different modeling disciplines emerged and what characterizes them with a discussion of the potential toward a common modeling future. It focuses on the areas of software, data, and process modeling and reports on an analysis of the research approaches, goals, and visions pursued in each, as well as the methods used. This analysis is based on the results of a survey conducted in the communities concerned, on a bibliometric study, and on interviews with a prominent representative of each of these communities. The paper discusses the different viewpoints of the communities, their commonalities and differences, and identifies possible starting points for further collaboration. It further discusses current challenges for the communities in general and modeling as a research topic in particular and highlights visions for the future. [ABSTRACT FROM AUTHOR]

Software quality is becoming a momentous challenge in software engineering processes, and software testing has a pivotal role in its measurements. Nature-inspired metaheuristic methods play an essential role in software testing, in which various studies have been conducted in this field; however, due to a lack of wide-ranging papers reviewing these methods, conducting a comprehensive systematic review to examine an array of crucial mechanisms in this field has become a necessity. This study aims to present a detailed analysis and taxonomically classifies the metaheuristic methods inspired by nature. This paper compromises a systematic literature review of 65 chosen studies published between 2015 and 2022. Genetic algorithm-based, hybrid, ant colony optimization-based, cuckoo search-based, firefly algorithm-based, artificial bee colony-based, and other metaheuristic methods constitute this systematic study's stratification. Evaluation methods, applied tools, merits, and demerits of each reviewed article are investigated. Additionally, future directions and open issues are addressed. This conducted paper not only expounds on software testing strengths, open issues, and future works, but also recognizes the quest for optimizing the insufficient metrics in software testing, such as mutation score, complexity, and scalability, which would be the propulsion of the testing process if consummated. [ABSTRACT FROM AUTHOR]

Component Based Software Engineering (CBSE) provides a way to create a new Component Based Software System (CBSS) by utilizing the existing components. The primary reason for that is to minimize the software development time, cost and effort. CBSS also increases the component reusability. Due to these advantages, software industries are working on CBSS and continuously trying to provide quality product. Usability is one of the major quality factors for CBSS. It should be measured before delivering the software product to the customer, so that if there are any usability flaws, it can be removed by software development team. In this paper, work has been done to evaluate the usability of CBSS based on major usability sub-factors (learnability, operability, understandability and configurability). For this purpose, firstly software metrics are identified for each usability sub-factor and the value of each sub-factor is evaluated for a component based software project. Secondly, overall usability of the software project is evaluated by using the calculated value of each usability sub-factor. Usability for the same project was also evaluated using Fuzzy approach in MATLAB to validate the experimental work of this research paper. It was identified that the value of usability obtained from software metrics and fuzzy model was very similar. This research work will be useful for the software developer to evaluate the usability of any CBSS and will also help them to compare different version of any CBSS in term of their usability. [ABSTRACT FROM AUTHOR]

Measuring software quality requires software engineers to understand the system’s quality attributes and their measurements. The quality attribute is a qualitative property; however, the quantitative feature is needed for software measurement, which is not considered during the development of most software systems. Many research studies have investigated different approaches for measuring software quality, but with no practical approaches to quantify and measure quality attributes. This paper proposes a software quality measurement model, based on a software interconnection model, to measure the quality of software components and the overall quality of the software system. Unlike most of the existing approaches, the proposed approach can be applied at the early stages of software development, to different architectural design models, and at different levels of system decomposition. This article introduces a software measurement model that uses a heuristic normalization of the software’s internal quality attributes, i.e., coupling and cohesion, for software quality measurement. In this model, the quality of a software component is measured based on its internal strength and the coupling it exhibits with other component(s). The proposed model has been experimented with nine software engineering teams that have agreed to participate in the experiment during the development of their different software systems. The experiments have shown that coupling reduces the internal strength of the coupled components by the amount of coupling they exhibit, which degrades their quality and the overall quality of the software system. The introducedmodel can help in understanding the quality of software design. In addition, it identifies the locations in software design that exhibit unnecessary couplings that degrade the quality of the software systems, which can be eliminated. [ABSTRACT FROM AUTHOR]

Software process improvement programs are partly founded on software measurement. However, despite their importance, it has been pointed out in the literature that many students are leaving the academic world without the necessary skills to conduct this kind of process. This can be understood by people's attitudes to this process which is regarded as time-consuming and difficult to understand—factors that explain the lack of interest in it during a student's academic life. In light of this, the application of serious games or gamification can show useful alternative ways of meeting this need, because the strategies they involve are well accepted by students and have a motivational and engaging effect on them. The objective of this work is to discover different approaches to the teaching of software measurement and software process improvement through gamification projects and serious games. This involves carrying out a systematic review of the literature, which is aimed at characterizing the state-of-the-art on the use of methods related to gamification and serious games in the abovementioned subjects. We conducted a systematic review of the literature to identify primary studies that address the use, planning, or evaluation of gamification, serious games, their features, and game mechanics in software engineering. We located 137 primary studies, published between 2000 and 2019. Although the use of serious games and gamification in software engineering is not recent, there still remains a large area to be explored, especially in software process improvement and software measurement. The study expands and advances the research on how serious games and gamification proposals can be used for teaching software measurement in the context of software process improvement programs by conducting a systematic review of the literature. [ABSTRACT FROM AUTHOR]

This survey reviews published materials related to the specific area of Search-Based Software Engineering that concerns software maintenance and, in particular, refactoring. The survey aims to give a comprehensive review of the use of search-based refactoring to maintain software. Fifty different papers have been selected from online databases to analyze and review the use of search-based refactoring in software engineering. The current state of the research is analyzed and patterns in the studies are investigated in order to assess gaps in the area and suggest opportunities for future research. The papers reviewed are tabulated in order to aid researchers in quickly referencing studies. The literature addresses different methods using search-based refactoring for software maintenance, as well as studies that investigate the optimization process and discuss components of the search. There are studies that analyze different software metrics, experiment with multi-objective techniques and propose refactoring tools for use. Analysis of the literature has indicated some opportunities for future research in the area. More experimentation of the techniques in an industrial environment and feedback from software developers is needed to support the approaches. Also, recent work with multi-objective techniques has shown that there are exciting possibilities for future research using these techniques with refactoring. This survey is beneficial as an introduction for any researchers aiming to work in the area of Search-Based Software Engineering with respect to software maintenance and will allow them to gain an understanding of the current landscape of the research and the insights gathered. [ABSTRACT FROM AUTHOR]

This paper discusses the definition, types, characteristic and construction of software metrics in the field of software development. Finally, an overview is given regarding the use of a software tool in software development in relation to software metrics in the field of banking. [ABSTRACT FROM AUTHOR]

Despite the acknowledged importance of quantitative security assessment in secure software development, current literature still lacks an efficient model for measuring internal software security risk. To this end, in this paper, we introduce a hierarchical security assessment model (SAM), able to assess the internal security level of software products based on low-level indicators, i.e., security-relevant static analysis alerts and software metrics. The model, following the guidelines of ISO/IEC 25010, and based on a set of thresholds and weights, systematically aggregates these low-level indicators in order to produce a high-level security score that reflects the internal security level of the analyzed software. The proposed model is practical, since it is fully automated and operationalized in the form of a standalone tool and as part of a broader Computer-Aided Software Engineering (CASE) platform. In order to enhance its reliability, the thresholds of the model were calibrated based on a repository of 100 popular software applications retrieved from Maven Repository. Furthermore, its weights were elicited in a way to chiefly reflect the knowledge expressed by the Common Weakness Enumeration (CWE), through a novel weights elicitation approach grounded on popular decision-making techniques. The proposed model was evaluated on a large repository of 150 open-source software applications retrieved from GitHub and 1200 classes retrieved from the OWASP Benchmark. The results of the experiments revealed the capacity of the proposed model to reliably assess internal security at both product level and class level of granularity, with sufficient discretion power. They also provide preliminary evidence for the ability of the model to be used as the basis for vulnerability prediction. To the best of our knowledge, this is the first fully automated, operationalized and sufficiently evaluated security assessment model in the modern literature. [ABSTRACT FROM AUTHOR]

[Context] Freelancers could catalyze the software development process by providing their niche skills to generate high quality outputs. They could help companies (including startups) to foster innovations by suggesting creative ideas and providing their expertise in implementing them (for instance, designing solutions, coding solutions etc.). Freelancers could effectively and efficiently work as a virtual member of the software development team. The company must make informed decisions about which task to allot to the freelancer, which freelancer to select, pricing the task, and evaluating the submitted work. On the other hand, the freelancer should make an informed decision about evaluating the monetary value of the task to be charged, trusting the requester, analyzing the skills requirement of the task (finding matches between skill requirement and skills processed), selecting the best task, and maintaining the highest level of reputation. However, the literature does not provide freelancers and the companies the guidelines that support their decision making. However, if freelancers are selected carefully for the most suitable task, the companies will benefit a lot in terms of improved software development metrics. [Objectives] The objective of this paper is to provide the research community the research trends in freelancer-supported software development. This helps to understand that which software development areas have higher concentrations of research efforts, which area has the support of empirical evidence to support management decision makings, and which area requires the research attention. [Method] The systematic study is conducted by planning the mapping protocol, executing the protocol, and reporting the findings using various visualization tools like bar charts and pie charts. The search process was planned to be executed using set of inclusion and exclusion conditions on four bibliographic databases (IEEExplore, Springerlink, Sciencedirect, and ACM digital library). The relevant papers are selected by applying inclusion and exclusion conditions. The google citations of the relevant papers are subject to the inclusion and exclusion conditions again to include the more relevant papers. Finally, the systematic schema was created and populated after analyzing the studies abstracts. [Results] The results indicate the following (a) The research focus is on generic software development (78%) rather on individual life cycle activities. (b) The number of empirical studies is limited (25%). (c) A number of studies proposing solutions and evaluating on live cases in industrial settings are missing from the literature. This is in comparison to the validation approaches (72%) i.e., solutions tested in laboratory settings. (d) At present, the literature has limited ability to provide the software companies (including startups) with the guidelines (in the form of opinions and experience reports) for involving freelancers in the software development process. (e) The reported challenges include Collaboration and Coordination (33%), Developer Recommendation (or selection) (19%), Team Formulation (14%), Task Recommendation (allocation) (14%), Task Decomposition (11%), Privacy and Security (Confidentiality) (11%), Budget Estimation (8%), Recognition (8%), Trust Issues (8%), Market Dynamism (6%), Intellectual Property Issues (6%), Participation of Crowd Worker (6%), and Capacity Utilization (3%). These challenges are highly interactive, and each challenge impacts all other challenges. (e) Recent focus of the researchers (total 7 studies in 2019) is on generic software development handling the collaboration and coordination (3 studies out of 7), Developer recommendation (2 studies out of 7), and task recommendation (2 studies out of 7). [Conclusion] The freelancer-driven software engineering research area has got the attraction of the researchers, but it will take a long time to gain maturity. This puts an urgent call for more empirical studies and evaluation-based solution research that could help companies (including startups) to foster innovations. Further, the research focus should be well distributed among the various development phases to address the unique challenges associated with individual activities. The accurate management of the freelancer in the software development could help companies and startups to foster innovations and remain competitive in the marketplace. [ABSTRACT FROM AUTHOR]

Various architectures can be applied in software design. The aim of this research is to examine a typical implementation of Jakarta EE monolithic and microservice software architectures in the context of software quality attributes. Software quality standards are used to define quality models, as well as quality characteristics and sub-characteristics, i.e., software quality attributes. This paper evaluates monolithic and microservice architectures in the context of Coupling, Testability, Security, Complexity, Deployability, and Availability quality attributes. The performed examinations yielded a quality-based mixed integer goal programming mathematical model for software architecture optimization. The model incorporates various software metrics and considers their maximal, minimal or targeted values, as well as upper and lower deviations. The objective is the sum of all deviations, which should be minimal. Considering the presented model, a solution which incorporated multiple monoliths and microservices was defined. This way, the internal structure of the software is defined in a consistent and symmetrical context, while the external software behavior remains unchanged. In addition, an intersection point of monolithic and microservice software architectures, where software metrics obtain the same values, was introduced. Within the intersection point, either one of the architectures can be applied. With the exception of some metrics, an increase in the number of features leads to a value increase of software metrics in microservice software architecture, whilst these values are constant in monolithic software architecture. An increase in the number of features indicated a quality attribute's importance for the software system should be examined and an appropriate architecture should be selected accordingly. Finally, practical recommendations regarding software architectures in terms of software quality were given. Since each software system needs to meet non-functional in addition to functional requirements, a quality-driven software engineering can be established. [ABSTRACT FROM AUTHOR]

Software quality is very important for the software engineering life cycle in the design stage. In this paper, the EA program draws UML and converts them to XMI format with software quality metrics applied. The Metrics for Object-Oriented Design (MOOD) involve Coupling Factor, Polymorphism Factor, Inheritance Factor Method, Hiding Factor Method, and Hiding Factor Attribute could have been measured in addition to inheritance factor methods and attributes. The intelligent techniques are used through fuzzy logic to determine the level of software quality depending on metrics. The application of fuzzy logic in software is flexible and updated according to system requirements. Software quality is very important for the software engineering life cycle in the design stage. [ABSTRACT FROM AUTHOR]

Software metrics have matured considerably during the last decade. There is still a lot to be done, but the progress during the last decade is obvious. The field has moved from being very much focused on measurement as such, for example, proposing new metrics, to study the application of measurement in software engineering, for example, empirical studies evaluating some method or technique. The pressure on delivering software products with the right functionality, on time, within budget and with the right quality means that there is a constant need for being able to measure success criteria are fulfilled. Measurement is needed to support, among other issues, understanding, monitoring, control, management, evaluation, improvement and predictability of all aspects related to software development. In order for software engineering to become a true engineering discipline, measurement and quantitative assessment are crucial. The articles included in the special section are very good examples of how software engineering is moving in this direction.

Design defects are symptoms of design decay, which can lead to several maintenance problems. To detect these defects, most of existing research is based on the definition of rules that represent a combination of software metrics. These rules are sometimes not enough to detect design defects since it is difficult to find the best threshold values; the rules do not take into consideration the programming context, and it is challenging to find the best combination of metrics. As an alternative, we propose in this paper to identify design defects using a genetic algorithm based on the similarity/distance between the system under study and a set of defect examples without the need to define detection rules. We tested our approach on four open-source systems to identify three potential design defects. The results of our experiments confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a content ontology design pattern for the representation of software metrics, in software engineering ontologies, called OOPMetrics. This content ontology design pattern is designed to ease the detection of software design aws based on the metrics that are defined in the ontology that uses it. We also present a case study that shows how an ontology that uses this pattern may be queried in order to detect these design aws. In particular, we will focus on the God Class design flaw. [ABSTRACT FROM AUTHOR]

The article analyzes previous studies on the results of Capability Maturity Model (CMM) program investments. One of these works is from Brad Clark, who investigated the relationship between the developer's CMM level and software development efficiency for 112 projects. Another by Donald Harter, Mayuram Krishnan and Sandra Slaughter analyzed 30 large-scale projects at a major information technology firm. The seven common performance metrics are also discussed including error density, software development productivity, percentage of rework, and schedule fidelity.

The microservice architecture is claimed to satisfy ongoing software development demands, such as resilience, flexibility, and velocity. However, developing applications based on microservices also brings some drawbacks, such as the increased software operational complexity. Recent studies have also pointed out the lack of methods to prevent problems related to the maintainability of these solutions. Disregarding established design principles during the software evolution may lead to the so-called architectural erosion, which can end up in a condition of unfeasible maintenance. As microservices can be considered a new architecture style, there are few initiatives to monitoring the evolution of software microservice-based architectures. In this paper, we introduce the SYMBIOTE method for monitoring the coupling evolution of microservice-based systems. More specifically, this method collects coupling metrics during runtime (staging or production environments) and monitors them throughout software evolution. The longitudinal analysis of the collected measures allows detecting an upward trend in coupling metrics that could represent signs of architectural degradation. To develop the proposed method, we performed an experimental analysis of the coupling metrics behavior using artificially generated data. The results of these experiment revealed the metrics behavior in different scenarios, providing insights to develop the analysis method for the identification of architectural degradation. We evaluated the SYMBIOTE method in a real-case open source project called Spinnaker. The results obtained in this evaluation show the relationship between architectural changes and upward trends in coupling metrics for most of the analyzed release intervals. Therefore, the first version of SYMBIOTE has shown potential to detect signs of architectural degradation during the evolution of microservice-based architectures. [ABSTRACT FROM AUTHOR]

Function point analysis (FPA) is a standardized method to systematically measure the functional size of software. This method is proposed by an international organization and it is currently recommended by governments and organizations as a standard method to be adopted for this type of measurement. This paper presents a compilation of improvements, focused on increasing the accuracy of the FPA method, which have been proposed over the past 13 years. The methodology used was a systematic literature review (SLR), which was conducted with four research questions aligned with the objectives of this study. As a result of the SLR, of the 1600 results returned by the search engines, 454 primary studies were preselected according to the criteria established for the SLR. Among these studies, only 18 specifically referred to accuracy improvements for FPA, which was the goal of this study. The low number of studies that propose FPA improvements might demonstrate the maturity of the method in the current scenario of software metrics. Specifically in terms of found issues, it was found that the step for calculating the functional size exhibited the highest number of problems, indicating the need to revise FPA in order to encompass the possible improvements suggested by the researchers. [ABSTRACT FROM PUBLISHER]

In Digital era, Software in electronic devices has become an indispensable to our daily life. Software Metric is a significant software engineering field that plays a quality role in software measurement. Better measurement and metrics are stepping stone to software growth with distinction. Moving from measurement to metrics is like moving from observation to understanding. Metrics are conceived by the user and designed to reveal a chosen characteristic in a reliable and meaning manner. Object Oriented Software is based on approach that works around the real-world entities and their characteristics. Object Oriented Software measurement is procedure in which calculations are done on real world entities to describe them according to clearly defined rules. Object Oriented Metric plays a vital role to find the efficiency of the software and improvement for future. The measurement of object oriented software seems to be a powerful tool for product effectiveness. This paper will analyze different Object Oriented Metrics and helpful for ensuring quality design with high cohesion and low coupling for advancement in Object Oriented Software Development. [ABSTRACT FROM AUTHOR]

The evolution of software is necessary for the success of software systems. Studying the evolution of software and understanding it is a vocal topic of study in software engineering. One of the primary concepts of software evolution is that the internal quality of a software system declines when it evolves. In this paper, the method of evolution of the internal quality of object-oriented open-source software systems has been examined by applying a software metric approach. More specifically, we analyze how software systems evolve over versions regarding size and the relationship between size and different internal quality metrics. The results and observations of this research include: (i) there is a significant difference between different systems concerning the LOC variable (ii) there is a significant correlation between all pairwise comparisons of internal quality metrics, and (iii) the effect of complexity and inheritance on the LOC was positive and significant, while the effect of Coupling and Cohesion was not significant. [ABSTRACT FROM AUTHOR]

A wide variety of software metrics focusing on various levels of abstraction and attributes have been recommended by the software research community. Several organizations have implemented comprehensive metrics programs to strengthen management decision making and enable continuous improvement of their software engineering processes. In spite of the enormous efforts made to bring about advancements in the field, industry's adoption of various metrics is still at a basic level and has not changed much over past 20+ years. Moreover, the project manager community still struggles to identify the right set of metrics pertaining to their specific needs and looking for guidelines on how to make the right usage of selected metric sets. There was a dedicated research effort made by the authors over past 3+ years to bring together various attributes of interest in software testing life cycle phases along the dimensions of effectiveness and efficiency with a special focus on the associations amongst these attributes. This research effort produced a software test metrics advisory tool, which project managers for software testing projects can depend upon as an advisory aid while making selection, usage and interpretation of various attributes and associated metric sets apt for their needs. This paper provides a view of the improvement which was observed in key performance indicators of 30 software testing projects in an IT organization when this test metrics advisory tool was closely deployed across all these projects. The paper also provides a view of the statistical validation exercise that was conducted to prove that the deployment of the tool actually resulted in improvements in all the identified indicators. It is highly expected that the research work undertaken by authors will contribute significantly towards filling up a crucial gap in the field of software test measures and metrics. Project managers for software testing projects will be able to effectively leverage the devised advisory tool for seeking answers to various questions running in their minds related to software measurement. [ABSTRACT FROM AUTHOR]

Software reliability assessment is important to evaluate and predict the reliability and performance of software system. The models applicable to the assessment of software reliability are called Software Reliability Growth Models (SRGMs). An SRGM provides a mathematical relationship between time span of testing or using the software and the cumulative number of faults detected. It is used to assess the reliability of the software during testing and operational phases. An important class of SRGM that has been widely studied is Non Homogeneous Poisson Process (NHPP). NHPP models are useful in describing failure processes, providing trends such as reliability growth and fault-content. Different NHPP models have been developed for different applications. In this paper, we described several existing software reliability growth models based on Non Homogeneous Poisson processes (NHPPs). This paper also addresses cost estimation models and cost functions that can be used to evaluate the cost of software during its development. [ABSTRACT FROM AUTHOR]

Context. Researchers proposing a new metric have the burden of proof to demonstrate to the research community that the metric is acceptable in its intended use. This burden of proof is provided through the multi-faceted, scientific, and objective process of software metrics validation. Over the last 40 years, however, researchers have debated what constitutes a "valid" metric. Aim. The debate over what constitutes a valid metric centers on software metrics validation criteria. The objective of this article is to guide researchers in making sound contributions to the field of software engineering metrics by providing a practical summary of the metrics validation criteria found in the academic literature. Method.We conducted a systematic literature review that began with 2,288 papers and ultimately focused on 20 papers. After extracting 47 unique validation criteria from these 20 papers, we performed a comparative analysis to explore the relationships amongst the criteria. Results. Our 47 validation criteria represent a diverse view of what constitutes a valid metric. We present an analysis of the criteria's categorization, conflicts, common themes, and philosophical motivations behind the validation criteria. Conclusions. Although the 47 validation criteria are not conflict-free, the diversity of motivations and philosophies behind the validation criteria indicates that metrics validation is complex. Researchers proposing new metrics should consider the applicability of the validation criteria in terms of our categorization and analysis. Rather than arbitrarily choosing validation criteria for each metric, researchers should choose criteria that can confirm that the metric is appropriate for its intended use. We conclude that metrics validation criteria provide answers to questions that researchers have about the merits and limitations of a metric. [ABSTRACT FROM AUTHOR]

Since several years, the Object Constraint Language (OCL) is a central component in modeling and transformation languages like the Unified Modeling Language, the Meta Object Facility, and Query View Transformation. Consequently, approaches MDE (Model-Driven Engineering) depend on this language. OCL is present not only in areas influenced by the OMG but also in the Eclipse Modeling Framework (EMF). Thus the quality of OCL and its realization in tools seems to be crucial for the success of model-driven development. Surprisingly, up to now a benchmark for OCL to measure quality properties has not been proposed. This paper puts forward in the first part the concepts of a comprehensive OCL benchmark. Our benchmark covers (1) OCL engine accuracy (e.g., for the handling of the undefined value, the use of variables and the implementation of OCL standard operations), (2) OCL engine determinateness properties (e.g., for the collection operations 'any' and 'flatten'), and (3) OCL engine efficiency (for data type and user-defined operations). In the second part, this paper empirically evaluates the proposed benchmark concepts by examining several OCL tools. The paper clarifies a number of differences in handling particular language features and under specifications in the OCL standard. [ABSTRACT FROM AUTHOR]

Complex software networks, as a typical kind of man-made complex networks, have attracted more and more attention from various fields of science and engineering over the past ten years. With the dramatic increase of scale and complexity of software systems, it is essential to develop a systematic approach to further investigate the complex software systems by using the theories and methods of complex networks and complex adaptive systems. This paper attempts to briefly review some recent advances in complex software networks and also develop some novel tools to further analyze complex software networks, including modeling, analysis, evolution, measurement, and some potential real-world applications. More precisely, this paper first describes some effective modeling approaches for characterizing various complex software systems. Based on the above theoretical and practical models, this paper introduces some recent advances in analyzing the static and dynamical behaviors of complex software networks. It is then followed by some further discussions on potential real-world applications of complex software networks. Finally, this paper outlooks some future research topics from an engineering point of view. [ABSTRACT FROM AUTHOR]

The process of measuring in software engineering has already been standardized in the ISO/IEC 15939 standard, where activities related to identifying, creating, and evaluating of measures are described. In the process of measuring software entities, however, an organization usually needs to create custom measurement systems, which are intended to collect, analyze, and present data for a specific purpose. In this paper, we present a proven industrial process for developing measurement systems including the artifacts and deliverables important for a successful deployment of measurement systems in industry. The process has been elicited during a case study at Ericsson and is used in the organization for over 3 years when the paper was written. The process is supported by a framework that simplifies the implementation of the measurement systems and shortens the time from the initial idea to a working measurement system by the factor of 5 compared with using a standard development process not tailored for measurement systems. Copyright © 2010 John Wiley & Sons, Ltd. The best measurement systems are those that address the direct information needs of their stakeholders. In this paper we present how such custom measurement systems can be easily developed and fully automated without the need for expensive tools, based on our experiences from Ericsson. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Component-Based Software Engineering (CBSE) has shown significant prospects in rapid production of large software systems with enhanced quality, and emphasis on decomposition of the engineered systems into functional or logical components with well-defined interfaces used for communication across the components. In this paper, a series of metrics proposed by various researchers have been analyzed, evaluated and benchmarked using several large-scale publicly available software systems. A systematic analysis of the values for various metrics has been carried out and several key inferences have been drawn from them. A number of useful conclusions have been drawn from various metrics evaluations, which include inferences on complexity, reusability, testability, modularity and stability of the underlying components. The inferences are argued to be beneficial for CBSE-based software development, integration and maintenance. [ABSTRACT FROM AUTHOR]

Software maintainability is a crucial property of software projects. It can be defined as the ease with which a software system or component can be modified to be corrected, improved, or adapted to its environment. The software engineering literature proposes many models and metrics to predict the maintainability of a software project statically. However, there is no common accordance with the most dependable metrics or metric suites to evaluate such nonfunctional property. The goals of the present manuscript are as follows: (i) providing an overview of the most popular maintainability metrics according to the related literature; (ii) finding what tools are available to evaluate software maintainability; and (iii) linking the most popular metrics with the available tools and the most common programming languages. To this end, we performed a systematic literature review, following Kitchenham's SLR guidelines, on the most relevant scientific digital libraries. The SLR outcome provided us with 174 software metrics, among which we identified a set of 15 most commonly mentioned ones, and 19 metric computation tools available to practitioners. We found optimal sets of at most five tools to cover all the most commonly mentioned metrics. The results also highlight missing tool coverage for some metrics on commonly used programming languages and minimal coverage of metrics for newer or less popular programming languages. We consider these results valuable for researchers and practitioners who want to find the best selection of tools to evaluate the maintainability of their projects or to bridge the discussed coverage gaps for newer programming languages. [ABSTRACT FROM AUTHOR]

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Web engineering is a new research line in software engineering that covers the definition of processes, techniques, and models suitable tot Web environments in order to guarantee the quality of results. The research community is working in this area and, as a very recent line, they are assuming the Model-Driven paradigm to support and solve some classic problems detected in Web developments. However, there is a lack in Web requirements treatment. This paper presents a general vision of Navigational Development Techniques (NDT), which is an approach to deal with requirements in Web systems. It is based on conclusions obtained in several comparative studies and it tries to fill some gaps detected by the research community. This paper presents its scope, its most important contributions, and offers a global vision of its associated tool: NDT-Tool. Furthermore, it analyzes how Web Engineering can be applied in the enterprise environment. NDT is being applied in real projects and has been adopted by several companies as a requirements methodology. The approach offers a Web requirements solution based on a Model-Driven paradigm that follows the most accepted tendencies by Web engineering. [ABSTRACT FROM AUTHOR]

• Methods to identify duplicate codes (code clones) are introduced. • All four types of clones can be identified. • Does not require external lexer or parser to process the code. • Less complex approach compared to AST and PDG based approaches. Software clone detection is an emerging research area in the field of software engineering. Software systems are subjected to continuous modifications in source code to improve the performance of the software, which may lead to code redundancy. Duplicate code/code clone is a piece of code reworked several times in software programs due to copy paste activity or reusability of existing software. Code clone is a prime subject in software evolution. Detection of software clones at the time of software evolution may improve the performance of software and reduce the maintenance cost and effort. This paper proposes metric based methods to detect code clones, as software clone is a universal problem in large scale programming environment. This paper introduces two metric based approaches to detect code clones by comparing (i) Control Statement Features (ii) Program Features like different types of statements, operators and operands. In order to demonstrate the effectiveness of the proposed approaches, extensive experiments are conducted on two datasets, C projects of Bellon's benchmark dataset and student lab programs (SLP).The methods efficiently identify similar functional clones. Proposed models only find similarity of whole programs but intelligent enough to highlight similar code segments across program files. [ABSTRACT FROM AUTHOR]

Production and maintenance of measurement models are crucial in the context of an advanced software development organization. The paper suggests a goal-oriented measurement model life cycle that, starting out from the high-level organizational goals, drives software organizations towards a structured process to deal with measurement models. The life cycle results into an integrated component of the management activities pointed to generate, refine, and fulfill the organizational goals, whereas the final measurement models become items of a purpose fitting measurement plan. For each phase of the life cycle, the main activities, with their inputs and outputs, are identified and, moreover, some driving templates are defined. The paper analyzes their interactions with the other software production and management-related tasks and proposes a possible allocation within an Experience Factory-based software organization. [ABSTRACT FROM AUTHOR]

No abstract received. [ABSTRACT FROM AUTHOR]

Evaluation of search engines relies on assessments of search results for selected test queries, from which we would ideally like to draw conclusions in terms of relevance of the results for general (e.g., future, unknown) users. In practice however, most evaluation scenarios only allow us to conclusively determine the relevance towards the particular assessor that provided the judgments. A factor that cannot be ignored when extending conclusions made from assessors towards users, is the possible disagreement on relevance, assuming that a single gold truth label does not exist. This paper presents and analyzes the predicted relevance model (PRM), which allows predicting a particular result's relevance for a random user, based on an observed assessment and knowledge on the average disagreement between assessors. With the PRM, existing evaluation metrics designed to measure binary assessor relevance, can be transformed into more robust and effectively graded measures that evaluate relevance towards a random user. It also leads to a principled way of quantifying multiple graded or categorical relevance levels for use as gains in established graded relevance measures, such as normalized discounted cumulative gain, which nowadays often use heuristic and data-independent gain values. Given a set of test topics with graded relevance judgments, the PRM allows evaluating systems on different scenarios, such as their capability of retrieving top results, or how well they are able to filter out non-relevant ones. Its use in actual evaluation scenarios is illustrated on several information retrieval test collections. [ABSTRACT FROM AUTHOR]

The term 'software crisis' refers to the huge amount of resources needed for the development and maintenance of software. A major problem with research in these areas is the lack of solid data. In particular, there is little data that can be used to predict the types of problems that are likely to occur during the software's maintenance. This paper describes the results of an analysis of a software system that underwent several revisions. Maintenance of the system was performed by distinct programming teams. At each revision of the software system, the analysis was performed by doing measurements of module complexity based on three quantities: Halstead's Software Science Effort, McCabe's cyclomatic complexity, and coupling analyses. The relationships of Software Science Effort to unchangeability of modules, of cyclomatic complexity to path coverage, and coupling analysis to measurement or modularity and coherence during the maintenance are given. [ABSTRACT FROM AUTHOR]

Summary: A design pattern is a general reusable solution to commonly recurring problems in software projects. Bad smells are symptoms existing in the source code that possibly indicate the presence of a structural problem that requires code refactoring. Although design pattern and bad smells be different concepts, literature has shown that they may be related and cooccur during the evolution of a software system. This paper presents an empirical study that investigates cooccurrences of design patterns and bad smells as well as identifies the main factors that contribute to the emergence of the relationship between them. We carried out a case study with five Java systems to: (1) investigate if the use of design pattern reduces bad smell occurrence, (2) identify cooccurrences of design patterns and bad smells, and (3) identify situations that contribute for the cooccurrence emergence. As the main result, we found that the application of design pattern not necessarily avoid bad smell occurrences. The results also show that some design patterns such as composite, factory method, and singleton, are intrinsically modular and might be useful in creating high‐quality systems. However, other design patterns such as adapter‐command, proxy, and state‐strategy, have presented high cooccurrence frequency with bad smells; therefore, they require attention in their implementation. Finally, via manual inspection in the components with cooccurrence, we found that the identified cooccurrences appeared due to poor planning and inadequate application of design patterns. [ABSTRACT FROM AUTHOR]

Finding an effective method for testing object-oriented software (OOS) has proven elusive in the software community due to the rapid development of object-oriented programming (OOP) technology. Although significant progress has been made by previous studies, challenges still exist in relation to the object distance measurement of OOS using Adaptive Random Testing (ART). This is partly due to the unique features of OOS such as encapsulation, inheritance and polymorphism. In a previous work, we proposed a new similarity metric called the Object and Method Invocation Sequence Similarity (OMISS) metric to facilitate multi-class level testing using ART. In this paper, we broaden the set of models in the metric (OMISS) by considering the method parameter and adding the weight in the metric to develop a new distance metric to improve unit testing of OOS. We used the new distance metric to calculate the distance between the set of objects and the distance between the method sequences of the test cases. Additionally, we integrate the new metric in unit testing with ART and applied it to six open source subject programs. The experimental result shows that the proposed method with method parameter considered in this study is better than previous methods without the method parameter in the case of the single method. Our finding further shows that the proposed unit testing approach is a promising direction for assisting software engineers who seek to improve the failure-detection effectiveness of OOS testing. [ABSTRACT FROM AUTHOR]

There are various root causes of software failures. Few years ago, software used to fail mainly due to functionality related bugs. That used to happen due to requirement misunderstanding, code issues and lack of functional testing. A lot of work has been done in past on this and software engineering has matured over time, due to which software‟s hardly fail due to functionality related bugs. To understand the most recent failures, we had to understand the recent software development methodologies and technologies. In this paper we have discussed background of technologies and testing progression over time. A survey of more than 50 senior IT professionals was done to understand root cause of their software project failures. It was found that most of the softwares fail due to lack of testing of non-functional parameters these days. A lot of research was also done to find most recent and most severe software failures. Our study reveals that main reason of software failures these days is lack of testing of non-functional requirements. Security and Performance parameters mainly constitute non-functional requirements of software. It has become more challenging these days due to lots of development in the field of new technologies like Internet of things (IoT), Cloud of things (CoT), Artificial Intelligence, Machine learning, robotics and excessive use of mobile and technology in everything by masses. Finally, we proposed a software development model called as T-model to ensure breadth and depth of software is considered while designing and testing of software. [ABSTRACT FROM AUTHOR]

Metrics have long been used to measure and evaluate software products and processes. Software product line architecture is a field in which few metrics have been applied, a surprising fact given the important role of software product line architecture in software product line development. Recently, Some metrics have been developed to assess software product line architecture. These metrics are useful but have not been widely used in industry. In this paper, some new metrics are provided to assess reusability of Software product line architecture. Our metrics are evaluated in action. [ABSTRACT FROM AUTHOR]

Software Testing is defined as a single phase activity in software development process by using water fall model. We have been working on a research project on formal software testing technique. In this paper, contrary to usual practice, a built-in testing module at each stage of software development process is proposed. The problem description is first expressed in a formal notation to get the problem-definition, in an unambiguous and formal format. This formal description of the problem will be more accurate and will form a formal test bed in the tabular form. This test bed will facilitate different types of relevant testing at each phase of system development process, both vertical and horizontal testing. The said software testing technique consists of three modules. This paper covers the work done in the first module. [ABSTRACT FROM AUTHOR]

As software systems are becoming increasingly complex, they need to dynamically and continually adapt their behavior to changing conditions in the long-term running. There will be large numbers of adaptations in these systems when evolving and the adaptations may be unknowable until system operation. To specify these adaptations, this paper proposes the mode-supported Linear Temporal Logic (mLTL) that is an effective way to describe global specifications of adaptive software. The global specifications are defined for adaptive software as requirements from the perspective of global adapting process. The model checking problem of mLTL is also resolved using Linear Temporal Logic (LTL) and Labelled Transition System Analyser (LTSA). Finally, we provide a prototype implementation for modelling and analyzing adaptive programs, and experimental evaluation shows feasibility and scalability of our approach. [ABSTRACT FROM AUTHOR]

When recommender systems present items, these can be accompanied by explanatory information. Such explanations can serve seven aims: effectiveness, satisfaction, transparency, scrutability, trust, persuasiveness, and efficiency. These aims can be incompatible, so any evaluation needs to state which aim is being investigated and use appropriate metrics. This paper focuses particularly on effectiveness (helping users to make good decisions) and its trade-off with satisfaction. It provides an overview of existing work on evaluating effectiveness and the metrics used. It also highlights the limitations of the existing effectiveness metrics, in particular the effects of under- and overestimation and recommendation domain. In addition to this methodological contribution, the paper presents four empirical studies in two domains: movies and cameras. These studies investigate the impact of personalizing simple feature-based explanations on effectiveness and satisfaction. Both approximated and real effectiveness is investigated. Contrary to expectation, personalization was detrimental to effectiveness, though it may improve user satisfaction. The studies also highlighted the importance of considering opt-out rates and the underlying rating distribution when evaluating effectiveness. [ABSTRACT FROM AUTHOR]

Model-driven Engineering (MDE) has attained great importance in both the Software Engineering industry and the research community, where it is now widely used to provide a suitable approach with which to improve productivity when developing software artefacts. In this scenario, measurement models (software artefacts) have become a fundamental point in improvement of productivity, where MDE and Software Measurement can reap mutual benefits. MDE principles and techniques can be used in software measurement to build more automatic and generic solutions, and to achieve this, it is fundamental to be able to develop software measurement models. To facilitate this task, a domain-specific language named 'Software Measurement Modelling Language' (SMML) has been developed. This paper tackles the question of whether the use of SMML can assist in the definition of software measurement models. An empirical study was conducted, with the aim of verifying whether SMML makes it easier to construct measurement models which are more usable and maintainable as regards textual notation. The results show that models which do not use the language are more difficult-in terms of effort, correctness and efficiency-to understand and modify than those represented with SMML. Additional feedback was also obtained, to verify the suitability of the graphical representation of each symbol (element or relationship) of SMML. [ABSTRACT FROM AUTHOR]

Software metrics help us to make meaningful estimates for software products and guide us in taking managerial and technical decisions. However, conventional static metrics have been found to be inadequate for modern object-oriented software due to the presence of object-oriented features such as polymorphism, dynamic binding, inheritance and unused code. This fact motivates us to focus on dynamic metrics in place of traditional static metrics. Moreover, dynamic metrics are more precise than static metrics as they are able to capture the dynamic behaviour of the software system during measurement. These dynamic metrics are usually obtained from the execution traces of the code or from the executable models. In this paper, advantages of dynamic metrics over static metrics are discussed and then a survey of the existing dynamic metrics is carried out. These metrics are characterized into different categories such as dynamic coupling metrics, dynamic cohesion metrics. Towards end of the paper, potential research challenges and opportunities in the field of dynamic metrics are identified. [ABSTRACT FROM AUTHOR]

This paper presents an approach to generate test cases from UML 2.0 sequence diagrams and subsequently prioritize those test cases using model information encapsulated in the sequence diagrams. The test cases generated according to the proposed approach satisfy the scenario coverage criterion and are suitable for system-level testing. For prioritizing test cases, three different prioritization metrics are proposed. The values of these prioritization metrics can be analytically computed from the model information only. This paper also presents an approach to generate test data using a concept called rule-based matrix. The prioritization metrics are used to control the number of test data without compromising the test adequacy. The effectiveness of the proposed approach has been verified using two industrial designs. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Software and system dependability is getting ever more important in embedded system design. Current industrial practice of model-based analysis is supported by state-transition diagrammatic notations such as Statecharts. State-of-the-art modeling tools like STATEMATE support safety and failure-effect analysis at design time, but restricted to qualitative properties, This paper reports on a (plug-in) extension of STATEMATE enabling the evaluation of quantitative dependability properties at design time. The extension is compositional in the way the model is augmented with probabilistic timing information. This fact is exploited in the construction of the underlying mathematical model, a uniform continuous-time Markov decision process, on which we are able to check requirements of the form: "The probability to hit a safety-critical system configuration within a mission time of 3 hours is at most 0.01." We give a detailed explanation of the construction and evaluation steps making this possible, and report on a nontrivial case study of a high- speed train signaling system where the tool has been applied successfully. [ABSTRACT FROM AUTHOR]

In this paper, we discuss basic issues related to measurements performed in the domains of software engineering and metrology. Similarities and differences are highlighted, with the aim of analyzing the level of knowledge in both domains. A theoretical approach is followed, allowing the proposal of a measurement process model, which is used as a guiding scheme throughout the paper to illustrate peculiarities arising when taking software measurements. Common ground between metrologists and software engineers are described by discussing foundations of measurement in the software context. [ABSTRACT FROM AUTHOR]

We present a methodology for Bayesian analysis of software quality. We cast our research in the broader context of constructing a causal framework that can include process, product, and other diverse sources of information regarding fault introduction during the software development process. In this paper, we discuss the aspect of relating internal product metrics to external quality metrics. Specifically, we build a Bayesian network (BN) model to relate object-oriented software metrics to software fault content and fault proneness. Assuming that the relationship can be described as a generalized linear model, we derive parametric functional forms for the target node conditional distributions in the BN. These functional forms are shown to be able to represent linear. Poisson, and binomial logistic regression, The models are empirically evaluated using a public domain data set from a software subsystem. The results show that our approach produces statistically significant estimations and that our overall modeling method performs no worse than existing techniques. [ABSTRACT FROM AUTHOR]

System performance is a key factor to take into account throughout the software life cycle of modern computer systems, mostly due to their typical characteristics such as distributed deployment, code mobility, and platform heterogeneity. An open challenge in this direction is to integrate the performance validation as a transparent and efficient activity in the system development process. Several methodologies have been proposed to automate the transformation of software/hardware models into performance models. In this paper, we do not take a transformational approach; rather, we present a framework to integrate a software model with a platform model in order to build a performance model. Performance indices are obtained from simulation of the resulting performance model. Our framework provides a library of predefined resource models, model annotation and integration procedures, and simulation support that makes the performance analysis a much easier activity. We present the results obtained from two different industrial case studies that show the maturity and the stability of our approach. [ABSTRACT FROM AUTHOR]