Successful Software Research Articles

How to Evaluate the Productivity of Software Ecosystem: A Case Study in GitHub

With the development of open source community, the software ecosystem has become a popular perspective in the research on software development process and environment. Software productivity is an important evaluation indicator of the software ecosystem health. A successful software ecosystem relies on long-term and stable production activities by the users, which ensures that the software ecosystem can continuously provide the value needed by users. Therefore, the measurement of software ecosystem productivity can help maintain the user development efficiency and the stability of the software ecosystem. However, there is still little literature on the productivity of open source software ecosystems. By analogy with the natural ecosystem, this paper gives the relevant definitions of software ecosystem productivity and analyzes the factors affecting the productivity of software ecosystem. Based on the factors of the ecosystem productivity and their interrelationships, this paper establishes a software ecosystem productivity model and takes the GitHub platform as an example for detailed analysis and explanation. The results show that the model can better explain the factors affecting the productivity of software ecosystems. It is helpful for the research on the measurement of the software ecosystem health and the software development efficiency.

Separation logic for sequential programs (functional pearl)

This paper presents a simple mechanized formalization of Separation Logic for sequential programs. This formalization is aimed for teaching the ideas of Separation Logic, including its soundness proof and its recent enhancements. The formalization serves as support for a course that follows the style of the successful Software Foundations series, with all the statement and proofs formalized in Coq. This course only assumes basic knowledge of lambda-calculus, semantics and logics, and therefore should be accessible to a broad audience.

Analysis on Detecting a Bug in a Software using Machine Learning

In today’s scenario, it is very essential in the development phase of a software, predicting a bug and to obtain a successful software. This can be achieved only through predicting some of the faults in the earlier phase itself such that, it can lead to have a reliable, efficient and a quality software. The challenging task here is to have a well sophisticated model that can predict the bug leading to a cost-effective software. In order to achieve this, few machine learning algorithms are used that produce accuracy with trained and test datasets. A variety of machine learning methodologies have been developed to learn and detect a bug in a software. In this paper, we perform the analysis on detecting a bug in a software using machine learning methods which is very much useful for Software Industries. It summarizes the existing work on detecting a bug in a software by providing the information about various methods involved in bug prediction and points out at the accuracy obtained by the existing methods, advantages, and the drawbacks while working with bug prediction.

The CECAM electronic structure library and the modular software development paradigm.

First-principles electronic structure calculations are now accessible to a very large community of users across many disciplines, thanks to many successful software packages, some of which are described in this special issue. The traditional coding paradigm for such packages is monolithic, i.e., regardless of how modular its internal structure may be, the code is built independently from others, essentially from the compiler up, possibly with the exception of linear-algebra and message-passing libraries. This model has endured and been quite successful for decades. The successful evolution of the electronic structure methodology itself, however, has resulted in an increasing complexity and an ever longer list of features expected within all software packages, which implies a growing amount of replication between different packages, not only in the initial coding but, more importantly, every time a code needs to be re-engineered to adapt to the evolution of computer hardware architecture. The Electronic Structure Library (ESL) was initiated by CECAM (the European Centre for Atomic and Molecular Calculations) to catalyze a paradigm shift away from the monolithic model and promote modularization, with the ambition to extract common tasks from electronic structure codes and redesign them as open-source libraries available to everybody. Such libraries include "heavy-duty" ones that have the potential for a high degree of parallelization and adaptation to novel hardware within them, thereby separating the sophisticated computer science aspects of performance optimization and re-engineering from the computational science done by, e.g., physicists and chemists when implementing new ideas. We envisage that this modular paradigm will improve overall coding efficiency and enable specialists (whether they be computer scientists or computational scientists) to use their skills more effectively and will lead to a more dynamic evolution of software in the community as well as lower barriers to entry for new developers. The model comes with new challenges, though. The building and compilation of a code based on many interdependent libraries (and their versions) is a much more complex task than that of a code delivered in a single self-contained package. Here, we describe the state of the ESL, the different libraries it now contains, the short- and mid-term plans for further libraries, and the way the new challenges are faced. The ESL is a community initiative into which several pre-existing codes and their developers have contributed with their software and efforts, from which several codes are already benefiting, and which remains open to the community.

The First Inherited Retinal Disease Registry in Iran: Research Protocol and Results of a Pilot Study.

To describe the protocol for developing a national inherited retinal disease (IRD) registry in Iran and present its initial report. This community-based participatory research was approved by the Ministry of Health and Medical Education of Iran in 2016. To provide the minimum data set (MDS), several focus group meetings were held. The final MDS was handed over to an engineering team to develop a web-based software. In the pilot phase, the software was set up in two referral centers in Iran. Final IRD diagnosis was made based on clinical manifestations and genetic findings. Ultimately, patient registration was done based on all clinical and non-clinical manifestations. Initially, a total of 151 data elements were approved with Delphi technique. The registry software went live at www. IRDReg.org based on DHIS2 open source license agreement since February 2016. So far, a total of 1001 patients have been registered with a mean age of 32.41±15.60 years (range, 3 months to 74 years). The majority of the registered patients had retinitis pigmentosa (42%, 95% CI: 38.9% to 45%). Genetic testing was done for approximately 20% of the registered individuals. Our study shows successful web-based software design and data collection as a proof of concept for the first IRD registry in Iran. Multicenter integration of the IRD registry in medical centers throughout the country is well underway as planned. These data will assist researchers to rapidly access information about the distribution and genetic patterns of this disease.

Discovering software developer's coding expertise through deep learning

The field of software development is growing rapidly and prevailing in every walk of life. The role of software developers in such a challenging and complex activity is very much important. The allocation of right software developers (i.e. who possesses appropriate coding skills) to projects is one of the crucial factors for successful software development. The problem is that it is very difficult for a client, project manager, as well as for software development organisations to find out an appropriate developer and assign him/her to a particular project. To achieve this, there is a need for such a sound mechanism that could detect the level of software developer coding expertise. This study has formulated criteria for novice and expert developers and carried out such criteria to discover the level of coding expertise of software developers using three different models of deep learning. These models include long short-term memory (LSTM), convolution 1D and hybrid (a combination of LSTM and convolution 1D). The deep learning models have analysed software developers’ previously written source code collected from the GitHub repository. An experiment was conducted to evaluate the performance of models. The results showed that the LSTM model performed better in comparison to other models by achieving 96.25% accuracy.

The UK Non-Frustration Rule: Should it be Replaced with a US-inspired Approach?

Recently, Arm Holdings, the most successful semiconductor and software design company in the UK, has agreed to be sold to SoftBank, a Japanese company. This takeover case, along with the case that Cadbury was acquired by Kraft in 2010, has led to questions about the openness to foreign mergers and acquisitions.[1] The non-frustration rule plays an important role in the openness of the UK’s market for corporate control.[2] Therefore, it is time to rethink about the non-frustration rule. One of the most heated questions is whether the rule should be replaced with the US-inspired approach. This article argues that the US-inspired approach will not function as well in the UK as it does in the US. After all, the UK and the US differ a lot in corporate structures and company regulations which make the background of the non-frustration rule different in two countries.

Better together: Elements of successful scientific software development in a distributed collaborative community.

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.

Research Framework of Evaluation Model to Assess the Effectiveness of Coordination Processes in Global Software Development Projects

Effective coordination is a vital aspect of successful Global Software Development (GSD) projects. Limited studies have examined coordination strategies and their related indicators. Therefore, this study focuses on assessing the coordination processes that require specific strategies and related indicators that can contribute to effective coordination. Thus, the main aim of this study is to present the entire research framework that will be used to formulate the Evaluation Model which consist of coordination strategies and related indicators. Research framework consists of three parts namely research philosophy, research design and research methods. Thus, this study elaborates each of this parts accordingly. The results of this study is the formulation of Evaluation Model to assess the effectiveness of the coordination processes in GSD projects.

Comparison of Machine Learning Techniques for Software Quality Prediction

Software quality prediction is one the most challenging tasks in the development and maintenance of software. Machine learning (ML) is widely being incorporated for the prediction of the quality of a final product in the early development stages of the software development life cycle (SDLC). An ML prediction model uses software metrics and faulty data from previous projects to detect high-risk modules for future projects, so that the testing efforts can be targeted to those specific ‘risky' modules. Hence, ML-based predictors contribute to the detection of development anomalies early and inexpensively and ensure the timely delivery of a successful, failure-free and supreme quality software product within budget. This article has a comparison of 30 software quality prediction models (5 technique * 6 dataset) built on five ML techniques: artificial neural network (ANN); support vector machine (SVMs); Decision Tree (DTs); k-Nearest Neighbor (KNN); and Naïve Bayes Classifiers (NBC), using six datasets: CM1, KC1, KC2, PC1, JM1, and a combined one. These models exploit the predictive power of static code metrics, McCabe complexity metrics, for quality prediction. All thirty predictors are compared using a receiver operator curve (ROC), area under the curve (AUC), and accuracy as performance evaluation criteria. The results show that the ANN technique for software quality prediction is promising for accurate quality prediction irrespective of the dataset used.

Social Network Analysis in Software Development Projects: A Systematic Literature Review

Software development in project teams has become more and more complex, with increasing demands for information and decision making. Software development in projects also hugely depends on effective interaction between people, and human factors have been identified as key to successful software projects. Especially in this context, managing and analyzing social networks is highly important. The instrument of social network analysis (SNA) provides fine-grained methods for analyzing social networks in project teams, going beyond the traditional tools and techniques of project management. This paper examines the importance of the application of SNA in software development projects. We conducted a systematic literature review (SLR) of research on software development projects and social network data published between 1980 and 2019. We identified and analyzed 86 relevant studies, finding that research on software development projects spans the topics of project organization, communication management, knowledge management, version and configuration management, requirement management, and risk management. Further, we show that most studies focus on project organization and that the most common method used to gather social data relies on automated extraction from various software development repositories in the SNA context. Our paper contributes to the software development literature by providing a broad overview of published studies on the use of social networks in helping software development projects. Finally, we identify research opportunities and make suggestions for addressing existing research gaps.

Machine-Learning Based Model for Improving Effort Estimation using Risk

In the real world, many software projects have effort, schedule, and cost overrun. Estimation accuracy is vital for a successful software project. This is a challenge since mostly there is a tendency to over-estimate or under-estimate the size and effort needed in a software project. The prediction of required effort is a critical activity, and a greater focus is required by considering additional factors such as project risk. The importance of project risks and their management has been indicated in literature and its consideration in effort estimation is the focus of this research. This research paper has a focus on proposing a model based on machine learning techniques for improving the effort estimation through inclusion of risk score. The proposed solution considered aggregating the capability of various machine learning models for prediction. The methodology involved usage of extreme gradient boosting algorithm. Data for the research included projects from industry standard dataset and organizational projects. The analysis revealed a reduction in the root mean square error values over multiple iterations suggesting an improvement in model performance. This reveals a better estimation due to minimization of the gap between estimated and actual efforts in a project. This, in turn, would enhance the potential for success of the project through an improved estimation process integrating risk score along with other parameters for estimation of software development effort. The feature importance chart also revealed that project risk score is an important attribute to be considered for effort estimation.

The contingent role of interproject connectedness in cultivating open source software projects

A better understanding of the key to successful open-source software (OSS) development continues to motivate research. Aligned with work that builds on the notion that an OSS development is tightly interrelated with its social environment (i.e., the OSS community), this study examines the relationship between interproject structure and OSS project success. OSS project success is reflected in two forms: popularity and knowledge creation. Extending the extant OSS literature, we theorize a contingent role of interproject connectedness. In particular, we posit three points: (1) an OSS project with more structural holes achieves higher popularity; (2) an OSS project with fewer structural holes yields higher knowledge creation; and (3) these two relationships are enhanced by an increase in project maturity. Using a dataset longitudinally collected from SourceForge.net, we found that OSS projects with widespread connectedness are more popular. This is especially so for those OSS projects in the mid-mature stage. We also found that OSS projects with a cohesive network achieve higher knowledge creation, irrespective of their maturity. Findings from our study can contribute to OSS literature by identifying OSS projects that are more likely to be successful.

Platform Provider Roles in Innovation in Software Service Ecosystems

Following a new innovation strategy, software vendors move their software onto their software service platforms and open up their platforms to third-party software service vendors. Although many studies state that enlarging the scope of software service offerings is the goal of the platform providers, only a few studies have focused on the roles that the platform providers take on to achieve the goal. These studies identified that the platform providers not only manage the platform but also promote and regulate third-party software. In this article, we extend this research by analyzing the activities of how platform providers promote innovation. For the analysis, we use empirical data about software services gathered from AppExchange of Salesforce. The analysis identifies the clusters and positions of software services of Salesforce and third-party vendors in the software service network. Our analysis results show that Salesforce promotes innovation by provisioning software services that are in core positions or that are bridges between service clusters. Third-party vendors release software services that are complementary to those of Salesforce. Overall, the results suggest that platform providers need to position strategically their software services to build successful software ecosystems, and that research on innovation needs to analyze the roles and efforts of the platform providers in detail.

Situational Modern Code Review Framework to Support Individual Sustainability of Software Engineers

Modern Code Review (MCR) is a socio-technical practice to improve source code quality and ensure successful software development. It involves the interaction of software engineers from different cultures and backgrounds. As a result, a variety of unknown situational factors arise that impact the individual sustainability of MCR team members and affect their productivity by causing mental distress, fear of unknown and varying situations. Therefore, the MCR team needs to be aware of the accurate situational factors, however, they are confronted with the issue of lack of competency in the identification of situational factors. This study aims to conduct the Delphi survey to investigate the optimal and well-balanced MCR-related situational factors. The conducted survey also aimed to recognize and prioritize the most influencing situational factors for MCR activities. The study findings reported 21 situational factors, 147 sub-factors, and 5 Categories. Based on the results of the Delphi survey the identified situational factors are transformed into the situational MCR framework. This study might be helpful to support the individual sustainability of the MCR team by making them aware of the situations that can occur and vary during the execution of the MCR process. This research might also help the MCR team to improve their productivity and sustain in the industry for longer. It can also support software researchers who want to contribute to situational software engineering from varying software engineering contexts.

A Bayesian Networks-Based Method to Analyze the Validity of the Data of Software Measurement Programs

Measures are essential resources to improve quality and control costs during software development. One of the main factors for having successful software measurement programs is measure trustworthiness, defined as how much a user can trust a measure to use it with confidence. Such confidence enables the users to interpret them and use them for supporting decision-making. ISO/IEC 15939:2007 describes four stages that influence such interpretability: measure selection, measure validation, threshold definition, and data validation. The literature is scarce in supporting data validation, which directly impacts the measure's trustworthiness value. This article aims to detail a method that uses Bayesian networks for supporting data validation and shows its application in practice to four software development projects from one company. The proposed method uses Bayesian networks to calculate the degree to which a collected number or symbol represents the real value for the measures and is integrated with GQM for assessing the measurement program's goals. First, the measurement users must create GQM model hierarchical structures, use it as input for constructing the Bayesian network, validate the Bayesian network, and, finally, use it to support decision-making. A tool to support the proposed method was developed and is freely available. Further, herein, the results of the case study are presented. We identified four benefits in using the proposed method: Externalization, Diagnosis support, Measure interpretation improvement, and Decision-making support. Given this, even though the initial effort to use the proposed method lasted, on average, one hour and fourteen minutes, the benefits of using it outweighed the effort of applying it. Therefore, our findings suggest that there was a positive intention in adopting the proposed method in practice.

Exploring UX Maturity in Software Development Environments in Saudi Arabia

User experience (UX) design is becoming increasingly crucial for developing successful software today. It can determine whether or not users stay engaged with a product or service. It is, therefore, important that organizations have their users in mind when developing software and that there is a maturity for UX work. However, there are still organizations which do not value UX highly and where UX maturity is low. This paper reported the results of a survey of 75 practitioners working in software-development environments in Saudi Arabia. The survey was conducted in July 2020 and aimed to explore practitioners' perceptions of UX maturity, UX significance, and the challenges that face UX process in software development environments. The results show a higher than expected perception of organizational UX maturity amongst the practitioners surveyed, with the majority considering their organizations to be at an "Integrated phase". The degree of awareness of UX value was also found higher than anticipated. Furthermore, the study reveals important information about the most used UX methods as task analysis, prototyping, and heuristic evaluation. It also shows that UX assessment and user involvement being considered during different stages of product development, particularly in the prototyping phase. The major challenges that face UX process were found to be the need to improve UX consistency and the ability of teams and departments to collaborate.

Productivity Metric Estimation: Comprehensive Examine of Efficiency, Effectiveness and Value Based Metrics

The successful software project estimation is critically dependent upon measurement of productivity. The overall goal of productivity metric is to improve the ability and performance of information system evaluation as well as maintenance, moreover determine the performance of the information technology (IT) performance in business applications. The activities corresponding to productivity metrics vary at distinct management levels. The activities used within project management leads to dynamic framework that could be opted in changing or dynamic environments. At the project management level, productivities activities concentrates on effectiveness. For instance, during estimation of effort required to reproduce a system, assumption must be manufacture regarding the ability that could lead to successful development of software project and this is fetched from past projects. This paper focuses on identifying the productivity metrics that are essential and appropriate for project management process.

Respect Human Value to Control Software Development Failure

People learn and define their own values to interact in different situations. It is important to know the human values (HV) for dealing humans in better ways. HV can also be helpful for software development managers to make right decisions for managing their teams well. Unfortunately, to a great extent, the very factor is ignored in software engineering (SE). This study aims to provide a basic motivation of the topic to SE researchers to carry out some empirical evidences to control software development failures through respecting software developers’ HV. In order to operationalize the study, few disciplines, in which the HV are empirically discussed, are considered to replicate the impacts on software development. The factor HVs is well connected with satisfaction and improvement outcomes in sociology, education and management studies. Likewise, this study also literates the importance of HVs for successful software project development. This study concludes that HV can form strong correlations with software development roles and can be used to minimize the software failure.

Exploring trends in the evolution of open-source systems

Software evolution is the costliest process in software project. Successful software projects tend to evolve longer for high quality software. To keep the software quality under control, software engineers need to know the trends in software growth to help in allocating appropriate resources in future releases. How does software evolve and in what pace is very important to understand software evolution? Knowing the evolution of software as a whole is not enough to make decisions. Software engineers need to understand the class evolution in object-oriented systems. The evolution of classes in five open-source systems are empirically studied using the growth rate using linear and nonlinear models. The work analyzes the evolution of classes for logarithmic, exponential and quadratic models. The results show that that most classes follow the logarithmic and quadratic models. While the linear model was the best fit in few number of classes. The systems evolution, measured using line of code and number of classes, also follows the logarithmic model for three out of five systems. These results show that nonlinear models are more common than linear model both at the class and system levels.