Improve Software Productivity Research Articles

Exploring Mobile Application User Experience Through Topic Modeling

Exploring user satisfaction and experience is the first step of software product improvement and business sustainability. The primary goal of this research is to investigate how companies can use topic modeling to understand mobile application user experience and offer methodological steps for identifying factors shaping it. SalesForce was selected for this case study as it was the most widely used CRM application in 2023. This study aims to uncover factors influencing positive and negative user experience, to compare and systematize them, and to indicate the business implications of topic modeling findings. For this study, authors collected 9081 online reviews of the SalesForce application from Google Play Store. The corpus is divided into three subsets based on the associated star ratings, where four and five stars indicate positive sentiment, three mixed sentiment, and two and one negative. Each subset is analyzed using the Latent Dirichlet Allocation algorithm, hyper-parameters were fine-tuned, and the experimental models were evaluated with coherence measures to determine the model with the optimal number of topics. The results indicate that the driving factors of positive experience are seamless functionality and reliability, design flaws and performance issues shape negative experiences, and mixed experiences arise from inconsistencies in usability and authentication challenges.

GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search

Code search aims to retrieve accurate code snippets based on a natural language query to improve software productivity and quality. With the massive amount of available programs such as (on GitHub or Stack Overflow), identifying and localizing the precise code is critical for the software developers. In addition, Deep learning has recently been widely applied to different code-related scenarios, e.g., vulnerability detection, source code summarization. However, automated deep code search is still challenging since it requires a high-level semantic mapping between code and natural language queries. Most existing deep learning-based approaches for code search rely on the sequential text i.e., feeding the program and the query as a flat sequence of tokens to learn the program semantics while the structural information is not fully considered. Furthermore, the widely adopted Graph Neural Networks (GNNs) have proved their effectiveness in learning program semantics, however, they also suffer the problem of capturing the global dependencies in the constructed graph, which limits the model learning capacity. To address these challenges, in this paper, we design a novel neural network framework, named GraphSearchNet, to enable an effective and accurate source code search by jointly learning the rich semantics of both source code and natural language queries. Specifically, we propose to construct graphs for the source code and queries with bidirectional GGNN (BiGGNN) to capture the local structural information of the source code and queries. Furthermore, we enhance BiGGNN by utilizing the multi-head attention module to supplement the global dependencies that BiGGNN missed to improve the model learning capacity. The extensive experiments on Java and Python programming language from the public benchmark CodeSearchNet confirm that GraphSearchNet outperforms current state-of-the-art works by a significant margin.

Improving the organization of cost analysis in a digital environment

Aim. The presented study aims to substantiate the need to use digital technologies in analytics to make effective management decisions and ensure cost reduction.Tasks. The authors determine the place of a digital analytics platform in cost analysis; identify key factors in the implementation of the platform; describe the capabilities of the tools provided by the analytics platform and how they can be used in the cost management process.Methods. This study uses general scientific methods; the results are summarized through tabular and graphical presentation; four groups of data analysis methods (descriptive and diagnostic analytics, predictive and prescriptive analytics) are identified; within each group, special methods of cost analysis (management analysis methods and advanced analytics methods) are indicated, which can be automated using an analytics platform.Results. It is found that the introduction of an analytics platform transforms the structure of time costs associated with performing analytical work, freeing up working time for solving other management tasks. It is established that analytics platforms are flexible and can find highly specialized solutions due to a wide range of tools and methods implemented on the basis of the platform. The selection of tools and methods depends on the tasks set as part of the cost analysis. Acceptable options for using advanced analytics tools within the framework of cost management are formulated.Conclusions. The analytical platform serves as a tool that improves the quality of data processing necessary for making effective management decisions. Comprehensive application of traditional cost analysis tools and advanced analytics facilitates the effective search for new opportunities for optimizing business processes, which is an urgent task in the context of uncertainty and limited resources. Considering the importance of the functions performed by the tools of the analytics platform and in connection with the withdrawal of foreign leaders in the field of business analytics from the Russian market due to sanctions, domestic vendors of analytical systems are faced with the challenge of further improving software products that should be on par with their foreign analogs in terms of functionality.

Customer Knowledge Management in Enterprise Software Development Companies: Organizational, Human and Technological Perspective

Abstract In this study, Knowledge-Based View (KBV) and Theory of Technology in a Generic Customer Knowledge Management (CKM) Framework were assimilated to demonstrate the Organizational, Human and Technological antecedent factors that enable CKM processes to improve software product quality. A Theoretical CKM Framework was developed by extracting Human, Organizational and Technological factors from the literature, then, the “Technique for Order of Preference by Similarity to Ideal Solution” (TOPSIS) Multi-Criteria Decision Making (MCDM) method was applied to find the importance level of factors to CKM development in software companies. The weight and priority of factors were determined by 31 experts in enterprise software development companies. The results show that, from an expert viewpoint, CKM antecedent factors are categorized into high priority and low priority groups. Organizational factors such as “Customer Involvement”, “Customer-Centric Culture” and “CKM Strategy Development” are high priority.

Enhancements to Neural Language Model for Generating System Configuration Code: A Study with Maven Dependency

Thanks to the widespread application of software frameworks, OTS components, DSLs, and new-generation software building and construction systems, leveraging system configuration code to assist in development is increasingly common, especially in agile software development. In software system implementation, the system configuration code is used to separate configuration items from the underlying logic, of which Maven dependency code is a typical example. To improve software productivity, developers often reuse existing dependency libraries. However, the large quantity, rapid iteration, and various application scenarios exacerbate the challenge for researchers to reuse the library efficiently and appropriately. Proper reuse of Maven dependencies requires correct importation, which is the research priority of this article; putting it into practical usage at the functional level is the next step. In order to overcome this barrier, researchers have proposed a number of recommendation and intelligent generation models based on deep learning algorithms and code learning strategies. We first introduce an enhancement path for the generation model in order to propose novel models that are more targeted than previous studies. We propose EMGSCC (Enhanced Model for Generating System Configuration Code), which generates accompanying dependency libraries based on the libraries already employed by the current system. EMGSCC uses content-based attention to cope with dependency language features and integrate additional domain information. Finally, we evaluate EMGSCC on the DDDI dataset with extra domain information, and findings show that improvement varies from 1% to 8% on all metrics compared with the baseline. We show empirical evidence of our enhancement path for generating system configuration code based on neural language models, and continuous improvement in this direction would yield promising results.

Application of a Gamified Approach to Learning in the Treatment of Problems in Software Process Improvement: Analysis and Discussion of Results

The specialized literature defines Software Process Improvement (SPI) as the fundamental approach to improving software products in software development organizations. In this context, studies report several problems and difficulties that organizations face during the implementation of improvements. Although there are studies that address the problems highlighted in improvement programs, few studies still seek to identify practices in order to mitigate the negative effects of critical factors. Thus, the objective of this work is to discuss and analyze the results obtained in an Experience Report that consisted of a dynamic application with gamification elements in an SPI context related to learning in the treatment of recurring problems or difficulties in the implementation of improvements. It is noteworthy that the dynamics can be developed in organizations (commercial and academic) regardless of the improvement model or standard desired, since it was elaborated in relation to common problems experienced and identified in the SPI implementations scenario. The analysis of the obtained data was performed using an Evaluation Framework for Gamification in Software Engineering, which provided a standard structure for the design of evaluation studies for gamification cases.

Offshore Software Maintenance Outsourcing: Predicting Client’s Proposal using Supervised Learning

In software engineering, software maintenance is the process of correction, updating, and improvement of software products after handed over to the customer. Through offshore software maintenance outsourcing (OSMO) clients can get advantages like reduce cost, save time, and improve quality. In most cases, the OSMO vendor generates considerable revenue. However, the selection of an appropriate proposal among multiple clients is one of the critical problems for OSMO vendors. The purpose of this paper is to suggest an effective machine learning technique that can be used by OSMO vendors to assess or predict the OSMO client’s proposal. The dataset is generated through a survey of OSMO vendors working in a developing country. The results showed that supervised learning-based classifiers like Naïve Bayesian, SMO, Logistics apprehended 69.75 %, 81.81 %, and 87.27 % testing accuracy respectively. This study concludes that supervised learning is the most suitable technique to predict the OSMO client's proposal.

Phacosurgery effects on the condition of corneal nerve fibers

The question of potential changes in the nerve fibers of the cornea induced by phacosurgery remains poorly investigated. This is a review of results of many individual studies aimed at assessing the condition of corneal nerve fibers following different techniques of cataract removal. The authors substantiate the necessity for structural analysis of fiber changes based on in vivo confocal microscopy of the cornea. Functional assessment of corneal sensitivity (esthesiometry) does not allow objective conclusions, for several reasons. Further studies on corneal nerve changes after phacosurgery could be directed at improving software products for objective quantitative assessment of corneal nerve fibers and analyzing long-term results of various phacosurgery techniques.

Implementation of Six Sigma Concept to Improve Software Product Quality at XYZ Company

Information technology is growing so rapidly that many companies need software products to improve the efficiency and effectiveness of company operations. XYZ Company, which manufactures various software products to meet market needs, is aware that there are deficiencies in the quality of its products. These deficiencies can reduce customer satisfaction and have a negative impact on the company. Therefore, we need a good method to improve product quality. One method of quality improvement that is widely studied and applied by companies is Six Sigma. Six Sigma is a method that focuses on improving product quality that will increase the effectiveness and efficiency of all operational processes to meet satisfaction and customer needs using five stages of DMAIC namely Define, Measure, Analyze, Improve, and Control. The results of using the Six Sigma method is a continuous improvement program that is expected to assist companies in improving the quality of software products produced by utilizing information systems. In this research, we first formulate problems based on references from previous studies, then design research sampling using the Six Sigma concept. Based on five stages of DMAIC, this research results seven recommendations to be implemented to improve the quality of software products at XYZ company. After all the recommendations in this research are implemented, the estimated sigma level of the quality software products increases by 0.46σ (3.71σ to 4.17σ).

Software quality assessment model: a systematic mapping study

Quality model is regarded as a well-accepted approach for assessing, managing and improving software product quality. There are three categories of quality models for software products, i.e., definition model, assessment model, and prediction model. Quality assessment model (QAM) is a metric-based approach to assess the software quality. It is typically regarded as of high importance for its clear method on how to assess a system. However, the current state-of-the-art in QAM research is under limited investigation. To address this gap, the paper provides an organized and synthesized summary of the current QAMs. In detail, we conduct a systematic mapping study (SMS) for structuring the relevant articles. We obtain a total of 716 papers from the five databases, and 31 papers are selected as relevant studies at last. In summary, our work focuses on QAMs from the following aspects: software metrics, quality factors, aggregation methods, evaluation methods and tool support. According to the analysis results, our work discovers five needs that researchers in this area should continue to address: (1) new method and criteria to tailor a quality framework (i.e., structure of software metrics and quality factors) according to different specifics, (2) systematic investigations on the effectiveness, strength and weakness of different aggregation methods to guide the method selection in different context, (3) more investigations on evaluating QAMs in the context of industrial cases, (4) further investigations or real-world case studies on the QAMs related tools, and (5) building a public and diverse software benchmark which can be adopted in different application context.

Are DevOps and Automation Our Next Silver Bullet?

Fred brooks, in his well-known classic The Mythical Man-Month , already told the software engineering industry in 1975 that there are no silver bullets in gaining an order-of-magnitude improvement in software productivity.1 He also observed that most of the big past gains in software productivity have come from removing artificial barriers that have made the accidental tasks inordinately hard, such as severe hardware constraints, awkward programming languages, lack of machine time. The hope and goal of software development processes in orchestrating the essential and accidental software engineering and development tasks is precisely to remove artificial barriers to delivering better, faster, cheaper software to the users. Our next silver bullet seems to have emerged as automating repeatable, manual process tasks. While, on one hand, we debate how to scale agile, on the other, we run to DevOps, continuous integration, and continuous delivery tools to achieve the so-called orders of magnitude of productivity improvement.

Characterizing and evaluating the quality of software process modeling language: Comparison of ten representative model-based languages

Software organizations are very conscious that deployments of well-defined software processes improve software product development and its quality. Over last decade, many Software Process Modeling Languages (SPMLs) have been proposed to describe and manage software processes. However, each one presents advantages and disadvantages. The main challenge for an organization is to choose the best and most suitable SPML to meet its requirements. This paper proposes a Quality Model (QM) which has been defined conforms to QuEF (Quality Evaluation Framework). This QM allows to compare model-based SPMLs and it could be used by organizations to choose the most useful model-based SPML for their particular requirements. This paper also instances our QM to evaluate and compare 10 representative SPMLs of the various alternative approaches (metamodel-level approaches; SPML based on UML and approaches based on standards). Finally, this paper concludes there are many model-based proposals for SPM, but it is very difficult to establish with could be the commitment to follow. Some non-considered aspects until now have been identified (e.g., validation within enterprise environments, friendly support tools, mechanisms to carry out continuous improvement, mechanisms to establish business rules and elements for software process orchestrating).

On the application of model-driven engineering in data reengineering

Model-Driven Engineering (MDE) emphasizes the systematic use of models to improve software productivity and some aspects of the software quality such as maintainability or interoperability. Model-driven techniques have proven useful not only as regards developing new software applications but also the reengineering of legacy systems. Models and metamodels provide a high-level formalism with which to represent artefacts commonly manipulated in the different stages of a software evolution process (e.g., a software migration) while model transformation allows the automation of the evolution tasks to be performed. Some approaches and experiences of model-driven software reengineering have recently been presented but they have been focused on the code while data reengineering aspects have been overlooked. The objective of this work is to assess to what extent data reengineering processes could also take advantage of MDE techniques.The article starts by characterising data-reengineering in terms of the tasks involved. It then goes on to state that MDE is particularly amenable as regards addressing the tasks previously identified. We present an MDE-based approach for the reengineering of data whose purpose is to improve the quality of the logical schema in a relational data migration scenario. As a proof of concept, the approach is illustrated for two common problems in data re-engineering: undeclared foreign keys and disabled constraints. This approach is organised following the three stages of a software reengineering process: reverse engineering, restructuring and forward engineering. We show how each stage is implemented by means of model transformation chains. A running example is used to illustrate each stage of the process throughout the article. The approach is validated with a real widely-used database. An assessment of the application of MDE in each stage is then presented, and we conclude by identifying the main benefits and drawbacks of using MDE in data reengineering.

Digitalisierung – Management Zwischen 0 und 1

Abstract Digitization as a process of expressing actions and values by codes 0 and 1 has already has become part of our lives. Digitization enables enterprises to improve production, sales and to increase volume of production. However, no standard digitization strategy has been yet developed. Even in the digitized business process management system, the most important position remains to a human being. The improvement of software products, their availability and the education system in the area of introduction and use of information technology is thus a striking feature in development of managing (but also other) current processes.

Toward a Customer Knowledge Management Model for Enhancing Enterprise Software Quality

Many previous research studies on software quality enhancement have only focused on the technical aspects of software quality, such as reliability, maintainability and functionality. However, the nature of enterprise software requires additional focus on transferring and integration of customer knowledge, for customization, enhancement, maintenance and training. As customers are seen as one of the most important stakeholders in software projects, it would appear that the current use of customer knowledge in software development is insufficient. This study concentrates on investigating Human, Organizational and Technological factors of customer knowledge management (CKM) in order to enhance the quality of enterprise software within software companies. After investigating CKM factors in the literature, a Technique for the Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to rank these factors regarding the importance of CKM development in enterprise software companies, in order to improve software product quality. The weight and priority of the factors were determined by 31 software development company experts. Based on the highest priority factors, a theoretical model was developed. This study proposes a fundamental model that can be used as a guideline for successful CKM applications, within enterprise software development companies, to improve software quality.

The research and practice of spacecraft software engineering

In order to ensure the safety and reliability of spacecraft software products, it is necessary to execute engineering management. Firstly, the paper introduces the problems of unsystematic planning, uncertain classified management and uncontinuous improved mechanism in domestic and foreign spacecraft software engineering management. Then, it proposes a solution for software engineering management based on system-integrated ideology in the perspective of spacecraft system. Finally, a application result of spacecraft is given as an example. The research can provides a reference for executing spacecraft software engineering management and improving software product quality.

Diversity in Subject Content Transfer Styles Improves Software Engineers' Analysis Skills

The learning profile of each student must be taken into account to offer the learning resources that best suits him/her and thus can benefit him/her more. In software engineering field, the learning process can be improved if lecturers use multiple content transfer styles to teach the same concept. The authors provide a case study to demonstrate that diversity in content transfer styles oriented to teaching software analysis techniques allows lecturers to improve students’ analysis capabilities. To validate the hypothesis, a case study was developed using a control group which learned software analysis techniques using the traditional method based on slides; for the second group the lecturers provided different subject content transfer styles in order to observe the improvement in the learning process when students have a choice of content transfer styles. Statistic techniques were used to validate the results. Due to the results obtained and analyzed, the authors can affirm that diversity in subject content transfer styles improves software engineers’ analysis skills, reflected in the quality improvement of software products developed.

Improving software product line using an ontological approach

Software product line (SPL) is an emergent strategy for generating software products. The variability and commonality of SPL is illustrated by feature models (FMs). The quality of software products relies on the correctness of SPL. The overall benefits of software product line engineering (SPLE) are reduced by various kinds of defects such as dead features and false optional features in an FM. These defects can be inherited in the software products built from a defective product line model (PLM). In this paper, the problem of enhancing the quality of software products derived from SPLE is handled. An ontological based approach is proposed following first-order logic (FOL) rules to identify defects namely dead features and false optional features. The classification of cases for these defects in FMs that represent variability of SPL is defined. The presented approach has been explained with the help of an FM derived from the standard case in product line (PL) community. The initial empirical evaluation of the proposed approach analyses 35 FMs with different sizes. The results obtained exhibit that the proposed approach is accurate, effective, scalable up to 200 features and therefore improves SPL.

ViSel-TR: A Novel Approach for Developing Component-based Vision Systems Working in Unstructured Environments

One of the main tasks of vision systems is to support autonomous vehicle navigation in unstructured environments, where unexpected objects can suddenly appear. For this, they can use various information sources (cameras, ultrasonic sensors, GPS systems, LIDAR, etc.) to model the environment in which they operate. All these data are combined to extract all the information needed to guide their movement through the environment. The complexity of this task prevents the integration of vision systems into real-time control systems (autonomous vehicles, mobile robots, etc.). This is because most of the research carried out within the computer vision field focuses on hardware development or on creating new algorithms and methods for performing the analysis and manipulation of the image data. However, system development issues are treated as secondary. Consequently, designs are very efficient but very little reusable. On the other hand, real-time systems possess features that make them particularly sensitive to whatever architectural decisions are made. The use of software frame works and components has demonstrated its effectiveness in improving software productivity and quality. This work proposes a novel approach, called ViSel-TR, for developing vision systems seeking two main objectives: (1) efficient interpretation and reasonable response time in an unstructured environment and (2) use of different development paradigms offered by software engineering that allow their integration in realtime systems. In order to achieve these objectives, ViSel-TR uses model driven software development techniques in order to separate the description of component-based real-time applications from their possible implementations for different platforms.

Model-driven synthesis of formally precise, stylized software architectures

Abstract Reliably producing software architectures in selected architectural styles requires significant expertise yet remains difficult and error-prone. Our research goals are to better understand the nature of style-specific architectures, and relieve architects of the need to produce such architectures by hand. To achieve our goals, this paper introduces a formally precise approach to separate architectural style design decisions from application-specific decisions, and then uses these separate decisions as inputs to an automated synthesizer. This in effect supports a model-driven development (MDD) approach to architecture synthesis with style as a separate design variable. We claim that it is possible to formalize this separation of concerns, long implicit in software engineering research; to automatically synthesize style-specific architectures; and thereby to improve software design productivity and quality. To test these claims, we employed a combination of experimental systems and case study methods: we developed an MDD tool and used it to carry out case studies using Kitchenham’s methods. Our contributions include: a theoretical framework formalizing our separation of concerns and synthesis approach; an MDD framework, Monarch; and results of case studies that we interpret as supporting our claims. This work advances our understanding of software architectural style as a formal refinement; makes application descriptions an explicit subject of study; and suggests that synthesis of architectures can improve software productivity and quality.