Large Software Products Research Articles

The Open-Source DeLiBA2 Hardware/Software Framework for Distributed Storage Accelerators

With the trend towards ever larger “big data” applications, many of the gains achievable by using specialized compute accelerators become diminished due to the growing I/O overheads. While there have been several research efforts into computational storage and FPGA implementations of the NVMe interface, to our knowledge there have been only very limited efforts to move larger parts of the Linux block I/O stack into FPGA-based hardware accelerators. Our hardware/software framework DeLiBA initially addressed this deficiency by allowing high-productivity development of software components of the I/O stack in user instead of kernel space and leverages a proven FPGA SoC framework to quickly compose and deploy the actual FPGA-based I/O accelerators. In its initial form, it achieves 10% higher throughput and up to 2.3× the I/Os per second (IOPS) for a proof-of-concept Ceph accelerator running in a real multi-node Ceph cluster. In DeLiBA2, we have extended the framework further to better support distributed storage systems, specifically by directly integrating the block I/O accelerators with a hardware-accelerated network stack, as well as by accelerating more storage functions. With these improvements, performance grows significantly: The cluster-level speed-ups now reach up to 2.8× for both throughput and IOPS relative to Ceph in software in synthetic benchmarks, and achieve end-to-end wall clock speed-ups of 20% for the real workload of building a large software package.

Analysis of Hybridized Techniques with Class Imbalance Learning for Predicting Software Maintainability

Software maintainability is a vital concern of organizations that develop and maintain large software products. The models that assess the maintainability of software systems at initial development stages play a significant role. In the Software Maintainability Prediction (SMP), a prevalent issue that needs to be taken care of is imbalanced data problem. For SMP, imbalanced data problem arises when the software classes that require high maintenance effort are less in number than classes that require low maintenance effort. In this paper, we dealt with the imbalanced data problem by the data resampling. With the imbalanced data, efficient machine learning algorithms are unable to predict the data points of both classes competently. Therefore, we examine the effectiveness of hybridized (HYB) techniques. The HYB techniques aid in finding an optimal solution for a problem by judging the goodness of multiple solutions. As per the results of the study, Adaptive synthetic minority oversampling technique (Adasyn) and Safe level synthetic minority oversampling technique (SafeSMOTE) are the best techniques of imbalanced data. Also, among the investigated HYB techniques, Fuzzy LogitBoost (GFS-LB) and Particle Swarm Optimization with Linear Discriminant Analysis (PSOLDA) emerged as the best techniques to predict maintainability.

Challenges of and Opportunities for a Large Diverse Software Team

A large software team consisting of members with different expertise, skillsets, personalities, ethnicities, and involving collaboration on a large and complex software product presents many technical and cultural challenges, but also provides unique opportunities. In this article, we discuss the essential issues we faced when successfully transforming a collection of various independently developed software libraries into one large integrated product: the eXtreme-scale scientific Software Development Kit (xSDK). We argue it is just as important to pay attention to cultural challenges, such as establishment of reliable communication channels that considers, among others, differences in personalities and backgrounds as well as overcoming geographical separation and time-zone distribution when collaborating, as technical challenges. Finally, we discuss opportunities stemming from participating in a large diverse software team, such as increased internal expertise, variety of skillsets, broadened connections to external experts, and access to a larger pool of ideas or solutions.

Selection of Accounting Software for Small and Medium Enterprises Using the Fuzzy Topsis Method

In the article, the authors analyse the types of accounting software available on the market, in order to identify the most effective programs in terms of meeting the needs of the accounting service of small and medium enterprises. Alternatives for the choice were: large software packages of corporate type, universal programs for automation of financial accounting, programs for the formation and submission of tax reporting, document automation systems, small programs with an emphasis on financial management. The ranking of types of accounting software in terms of compliance with the requirements of accounting services of small and medium enterprises was led by universal programs focused primarily on the implementation of financial accounting functions with maximum support of the accounting process.

Novel Multi-Perspective Usability Evaluation Framework for Selection of Open Source Software Based on BWM and Group VIKOR Techniques

Increasing demand for open-source software (OSS) has raised the value of efficient selection in terms of quality; usability is an essential quality factor that significantly affects system acceptability and sustainability. Most large and complex software packages partitioned across multiple portals and involve many users — each with their role in the software package; those users have different perspectives on the software package, defined by their knowledge, responsibilities, and commitments. Thus, a multi-perspective approach has been used in usability evaluation to overcome the challenge of inconsistency between users’ perspectives; the inconsistency challenge would lead to an ill-advised decision on the selection of a suitable OSS. This study aimed to assist the public and private organizations in evaluating and selecting the most suitable OSS. The evaluation of the OSS software packages to choose the best one is a challenging task owing to (a) multiple evaluation criteria, (b) criteria importance, and (c) data variation; thus, it is considered a sophisticated multi-criteria decision making (MCDM) problem; moreover, the multi-perspective usability evaluation framework for OSS selection lacks in the current literature. Hence, this study proposes a novel multi-perspective usability evaluation framework for the selection of OSS based on the multi-criteria analysis. Integration of best-worst method (BWM) and VIKOR MCDM techniques has been used for weighting and ranking OSS alternatives. BWM is utilized for weighting of evaluation criteria, whereas VIKOR is applied to rank OSS-LMS alternatives. Individual and group decision-making contexts, and the internal and external groups aggregation were used to demonstrate the efficiency of the proposed framework. A well-organized algorithmic procedure is presented in detail, and a case study was examined to illustrate the validity and feasibility of the proposed framework. The results demonstrated that BWM and VIKOR integration works effectively to solve the OSS software package benchmarking/selection problems. Furthermore, the ranks of OSS software packages obtained from the VIKOR internal and external group decision making were similar; the best OSS-LMS based on the two ways was ‘Moodle’ software package. Among the scores of groups in the objective validation, significant differences were identified; this indicated that the ranking results of internal and external VIKOR group decision making were valid, which pointed to the validation of the framework.

Reparation in Evolutionary Algorithms for Multi-objective Feature Selection in Large Software Product Lines

Software Product Lines Engineering is the area of software engineering that aims to systematise the modelling, creation and improvement of groups of interconnected software systems by formally expressing possible alternative products in the form of Feature Models. Deriving a software product/system from a feature model is called Feature Configuration. Engineers select the subset of features (software components) from a feature model that suits their needs, while respecting the underlying relationships/constraints of the system–which is challenging on its own. Since there exist several (and often antagonistic) perspectives on which the quality of software could be assessed, the problem is even more challenging as it becomes a multi-objective optimisation problem. Current multi-objective feature selection in software product line approaches (e.g., SATIBEA) combine the scalability of a genetic algorithm (IBEA) with a solution reparation approach based on a SAT solver or one of its derivatives. In this paper, we propose MILPIBEA, a novel hybrid algorithm which combines IBEA with the accuracy of a mixed-integer linear programming (MILP) reparation. We show that the MILP reparation modifies fewer features from the original infeasible solutions than the SAT reparation and in a shorter time. We also demonstrate that MILPIBEA outperforms SATIBEA on average on various multi-objective performance metrics, especially on the largest feature models. The other major challenge in software engineering in general and in software product lines, in particular, is evolution. While the change in software components is common in the software engineering industry, the particular case of multi-objective optimisation of evolving software product lines is not well-tackled yet. We show that MILPIBEA is not only able to better take advantage of the evolution than SATIBEA, but it is also the one that continues to improve the quality of the solutions when SATIBEA stagnates. Overall, IBEA performs better when combined with MILP instead of SAT reparation when optimising the multi-objective feature selection in large and evolving software product lines.

Software Deterioration Control Based on Issue Reports

Introduction: Successive code changes during the maintenance phase may cause the emergence of bad smells and anti-patterns in code and gradually results in deterioration of the code and difficulties in its maintainability. Continuous Quality Control (QC) is essential in this phase to refactor the anti-patterns and bad smells. Objectives: The objective of this research has been to present a novel component called Code Deterioration Watch (CDW) to be integrated with existing Issue Tracking Systems (ITS) in order to assist the QC team in locating the software modules most vulnerable to deterioration swiftly. The important point regarding the CDW is the fact that its function has to be independent of the code level metrics rather it is totally based on issue level metrics measured from ITS repositories. Methods: An issue level metric that properly alerts us of bad-smell emergence was identified by mining software repositories. To measure that metric, a Stream Clustering algorithm called ReportChainer was proposed to spot Relatively Long Chains (RLC) of incoming issue reports as they tell the QC team that a concentrated point of successive changes has emerged in the software. Results: The contribution of this paper is partly creating a huge integrated code and issue repository of twelve medium and large size open-source software products from Apache and Eclipse. By mining this repository it was observed that there is a strong direct correlation (0.73 on average) between the number of issues of type “New Feature” reported on a software package and the number of bad-smells of types “design” and “error prone” emerged in that package. Besides a strong direct correlation (0.97 on average) was observed between the length of a chain and the magnitude of times it caused changes to a software package. Conclusion: The existence of direct correlation between the number of issues of type “New Feature” reported on a software package and (1) the number of bad-smells of types “design” and “error prone” and (2) the value of “CyclomaticComplexity” metric of the package, justifies the idea of Quality Control merely based on issue-level metrics. A stream clustering algorithm can be effectively applied to alert the emergence of a deteriorated module.

Опыт применения механизма областей для поэтапного распараллеливания программных комплексов с помощью системы SAPFOR

Одна из основных сложностей разработки параллельной программы для кластера — необходимость принятия глобальных решений по распределению данных и вычислений с учетом свойств всей программы, а затем выполнения кропотливой работы по модификации программы и ее отладки. Большой объем программного кода, а также многомодульность, многовариантность и многоязыковость, затрудняют принятие решений по согласованному распределению данных и вычислений. Опыт использования предыдущей системы САПФОР показал, что при распараллеливании на кластер больших программ и программных комплексов необходимо уметь распараллеливать их постепенно, начиная с наиболее времяемких фрагментов и постепенно добавляя новые фрагменты, пока не достигнем желаемого уровня эффективности параллельной программы. С этой целью предыдущая система была полностью переработана, и на ее основе была создана новая система SAPFOR (System FOR Automated Parallelization). В данной статье будет рассмотрен опыт применения метода частичного распараллеливания, идея которого заключается в том, что распараллеливанию подвергается не вся программа целиком, а ее части (области распараллеливания) — в них заводятся дополнительные экземпляры требуемых данных, производится распределение этих данных и соответствующих им вычислений. The main difficulty in developing a parallel program for a cluster is the need to make global decisions on the distribution of data and computations, taking into account the properties of the entire program, and then doing the hard work of modifying the program and debugging it. A large amount of code as well as multimoduling, multivariant and multilanguage, make it difficult to make decisions on a consistent distribution of data and computations. The experience of using the previous SAPFOR system showed that, when parallelizing large programs and software packages for a cluster, one should be able to parallelize them gradually, starting with the most time-intensive fragments and gradually adding new fragments until we reach the desired level of parallel program efficiency. For this purpose, the previous system was completely redesigned and a new system SAPFOR (System FOR Automated Parallelization) was created. To solve this problem, the method of incremental or partial arallelization will be considered in this paper. The idea of this method is that not the entire program is subjected to parallelization, but only its parts (parallelization regions) where additional versions of the required data are created and distributed and the corresponding computations are performed. This paper also discusses the application of automated mapping of programs to a cluster using the proposed incremental parallelization method and using the example of a NPB (NAS Parallel Benchmarks) software package.

Do scaling agile frameworks address global software development risks? An empirical study

Driven by the need to coordinate activities of multiple agile development teams cooperating to produce a large software product, software-intensive organizations are turning to scaling agile software development frameworks. Despite the growing adoption of various scaling agile frameworks, there is little empirical evidence of how effective their practices are in mitigating risk, especially in global software development (GSD), where project failure is a known problem.In this study, we develop a GSD Risk Catalog of 63 risks to assess the degree to which two scaling agile frameworks–Disciplined Agile Delivery (DAD) and the Scaled Agile Framework (SAFe)–address software project risks in GSD. We examined data from two longitudinal case studies implementing each framework to identify the extent to which the framework practices address GSD risks.Scaling agile frameworks appear to help companies eliminate or mitigate many traditional risks in GSD, especially relating to users and customers. However, several important risks were not eliminated or mitigated. These persistent risks in the main belonged to the Environment quadrant highlighting the inherent risk in developing software across geographic boundaries. Perhaps these frameworks (and arguably any framework), would have difficulty alleviating, issues that appear to be outside the immediate control of the organization.

A Large Software System Test Engineering Practices

The following Paper describes the experiences of a test engineering team, which had worked with a large software product development and support activity. This team has studied the existing software product, available test tools, test environment, with an objective of analyzing existing testing processes and methodologies for this large software product. The Paper discusses a number of initiatives and recommendations made by this test engineering group aimed at increasing the testing efficiency, optimizing the test suites, measuring and improving effectiveness of test cases and the quantifiable benefits and process improvements, that can derived from such initiatives. This activity was undertaken as part of a test engineering initiative to bring in place a set of innovative test engineering practices as potential business value drivers.

F90wrap: an automated tool for constructing deep Python interfaces to modern Fortran codes

f90wrap is a tool to automatically generate Python extension modules which interface to Fortran libraries that makes use of derived types. It builds on the capabilities of the popular f2py utility by generating a simpler Fortran 90 interface to the original Fortran code which is then suitable for wrapping with f2py, together with a higher-level Pythonic wrapper that makes the existance of an additional layer transparent to the final user. f90wrap has been used to wrap a number of large software packages of relevance to the condensed matter physics community, including the QUIP molecular dynamics code and the CASTEP density functional theory code.

Sandbox Environments in an ERP System Context: Examining User Attitude and Satisfaction

Enterprise Resource Planning (ERP) systems are large complex software packages that are widely usedin organizations today. Due to the complexity, sandbox environments are often used for training on ERPsystems. This study examines the effectiveness of training using a sandbox environment, specifically examininguser attitudes, users’ perception of financial knowledge gained, and user satisfaction in the ERP environmentwhile learning financial concepts. The theoretical framework is based on the Technology Acceptance Modelwith additional factors predicted to impact users’ attitude and satisfaction. Data were collected from 124subjects who voluntarily participated in the study. Structural Equation Modeling was used to analyze the data.The results of this study should help organizations understand the factors impacting user attitudes on ERPtraining leading to higher self‑perception of knowledge gains and task completion satisfaction. In addition, thisstudy provides the results of using ERP systems as a tool for financial education.

A Parallel Framework of Combining Satisfiability Modulo Theory with Indicator-Based Evolutionary Algorithm for Configuring Large and Real Software Product Lines

Multi-objective evolutionary algorithm (MOEA) has been widely applied to software product lines (SPLs) for addressing the configuration optimization problems. For example, the state-of-the-art SMTIBEA algorithm extends the constraint expressiveness and supports richer constraints to better address these problems. However, it just works better than the competitor for four out of five SPLs in five objectives and the convergence speed is not significantly increased for largest Linux SPL from 5 to 30[Formula: see text]min. To further improve the optimization efficiency, we propose a parallel framework SMTPORT, which combines four corresponding SMTIBEA variants and performs these variants by utilizing parallelization techniques within the limited time budget. For case studies in LVAT repository, we conduct a series of experiments on seven real-world and highly-constrained SPLs. Empirical results demonstrate that our approach significantly outperforms the state-of-the-art for all the seven SPLs in terms of a quality Hypervolume metric and a diversity Pareto Front Size indicator.

Methods for automated formation of a disassembled listing structure

The subject of the research is the method of splitting a disassembled code into logical blocks in automatic mode, searching for software vulnerabilities without using source code (using a binary file or its equivalent, obtained by reverse engineering).The object of the research is the existing code analyzers and features of their functionality.The aim of the study is to consider the possibility of splitting a disassembled code into logical blocks in automatic mode and some of the possible difficulties associated with this.Formulation of the problem. The complexity of analyzing large software products at the level of machine code necessitates the automation of this process. The research methodology is based on a combination of theoretical and empirical approaches using the methods of static and dynamic analysis, comparison, generalization, algorithmization, modeling, synthesis. Key findings. Splitting the code into blocks by sequential in line-by-line analysis of machine code in some cases can lead to misinterpretation. In addition, the analysis of the code according to the conclusions of the functions also does not guarantee the correctness of the determination of the boundaries of the functions. However, in general, the matrix method can be applied to analyze the dependencies of functions on the blocks of code thus selected.The scientific novelty is connected with the determination of promising vectors for the study of software code for vulnerability, the rationale for the approach (building the transition matrix from integer values), which may be the initial stage of preparation for the automated analysis of the disassembled code.

A parallel portfolio approach to configuration optimization for large software product lines

SummarySoftware product line (SPL) engineering demands for optimal or near‐optimal products that balance multiple often competing and conflicting objectives. A major challenge for large SPLs is to efficiently explore a huge space of various products and satisfy a large number of predefined constraints simultaneously. To improve the optimality and convergence speed, we propose a parallel portfolio approach, called IBEAPORT, which designs three algorithm variants by incorporating constraint solving into the indicator‐based evolutionary algorithm in different ways and performs these variants by utilizing parallelization techniques. Our approach utilizes the exploration capabilities of different algorithms and improves optimality as far as possible within a limited time budget. We evaluate our approach on five large‐scale real‐world SPLs. Empirical results demonstrate that our approach significantly outperforms the state of the art for all five SPLs on a quality indicator and a diversity indicator. Moreover, IBEAPORT quickly converges to a relatively stable hypervolume value even for the largest SPL with 6888 features.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds.

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

POINTO - A LOW COST SOLUTION TO POINT CLOUD PROCESSING

Abstract. With advance in technology access to data especially 3D point cloud data becomes more and more an everyday task. 3D point clouds are usually captured with very expensive tools such as 3D laser scanners or very time consuming methods such as photogrammetry. Most of the available softwares for 3D point cloud processing are designed for experts and specialists in this field and are usually very large software packages containing variety of methods and tools. This results in softwares that are usually very expensive to acquire and also very difficult to use. Difficulty of use is caused by complicated user interfaces that is required to accommodate a large list of features. The aim of these complex softwares is to provide a powerful tool for a specific group of specialist. However they are not necessary required by the majority of the up coming average users of point clouds. In addition to complexity and high costs of these softwares they generally rely on expensive and modern hardware and only compatible with one specific operating system. Many point cloud customers are not point cloud processing experts or willing to spend the high acquisition costs of these expensive softwares and hardwares. In this paper we introduce a solution for low cost point cloud processing. Our approach is designed to accommodate the needs of the average point cloud user. To reduce the cost and complexity of software our approach focuses on one functionality at a time in contrast with most available softwares and tools that aim to solve as many problems as possible at the same time. Our simple and user oriented design improve the user experience and empower us to optimize our methods for creation of an efficient software. In this paper we introduce Pointo family as a series of connected softwares to provide easy to use tools with simple design for different point cloud processing requirements. PointoVIEWER and PointoCAD are introduced as the first components of the Pointo family to provide a fast and efficient visualization with the ability to add annotation and documentation to the point clouds.

SMTIBEA: a hybrid multi-objective optimization algorithm for configuring large constrained software product lines

A key challenge to software product line engineering is to explore a huge space of various products and to find optimal or near-optimal solutions that satisfy all predefined constraints and balance multiple often competing objectives. To address this challenge, we propose a hybrid multi-objective optimization algorithm called SMTIBEA that combines the indicator-based evolutionary algorithm (IBEA) with the satisfiability modulo theories (SMT) solving. We evaluated the proposed algorithm on five large, constrained, real-world SPLs. Compared to the state-of-the-art, our approach significantly extends the expressiveness of constraints and simultaneously achieves a comparable performance. Furthermore, we investigate the performance influence of the SMT solving on two evolutionary operators of the IBEA.

Innovations on Bayesian Approaches of Software Cost Estimation Model

Objectives: To find Large Software Products Cost Estimation Model by using Bayesian Approaches. Methods/Statistical Analysis: Composite strategy for building programming models in view of a blend of information and master judgment is tried here. This system depends on the surely knew and generally acknowledged Bayes’ hypothesis that has been effectively connected in other building areas incorporating to some degree in the product unwavering quality designing space. Be that as it may, the Bayesian methodology has not been viably misused for building more powerful programming estimation models that utilization a change adjusted blend of undertaking information and master judgment. The center of this paper is to demonstrate the change in precision of the cost estimation model when the Bayesian methodology is utilized versus the numerous relapse approach. Findings: We employed Bayesian model aligned utilizing a dataset of 100 datapoints approved on a dataset of 200 datapoints (sample data), it yields an expectation exactness of PRED(.30) = 76% (i.e., 106 or 76% of the 200 datapoints are evaluated inside 29.5% of the actuals). The immaculate relapse based model aligned utilizing 100 datapoints when accepted on the same 200 task dataset yields a poorer precision of PRED(.30) = 53.4%. Application/Improvements: This Paper Very Advanced Approach for Large Industrial Software Products.

Examining the motivators of training transfer in an enterprise systems context

ABSTRACTEnterprise systems (ES) are large software packages that have been widely adopted, but are complex to deploy. One way to obtain more value from them is to train end-users. However, little is known about the effectiveness of ES training. This study examines post-training behaviour in the ES environment through the concept of training transfer and the theoretical framework of self-determination theory. It proposes that end-users’ computer self-efficacy (CSE) and mastery orientation (MO), as well as the perceived ease-of-use (PEOU) of a system, influence their motivation to transfer the skills they have gained during training to their work environment and to use the system. Data was collected from 170 ES end-users, who had previously attended ES training, through a survey. Partial least squares modelling was used to analyse the data, and all of the hypotheses were supported. This study is among the first few studies that investigate the more distal impact of information systems training.