Small Software Projects Research Articles

Application of Generative Artificial Intelligence for Risk Management of Software Projects

The article highlights an innovative approach to risk management in software projects using generative artificial intelligence. It describes a methodology that involves the use of publicly available chatbots to identify, analyze, and prioritize risks. The Crawford method is used as a basis for risk identification. The authors propose specific formulations of requests to chatbots (instructs, prompts) that facilitate obtaining the necessary information. The effectiveness of the methodology has been demonstrated on five small software projects and a dozen of economic and organizational projects of significantly different scales, from small to federal. This confirms its applicability and practical value.

Method of Domestic Small and Medium-sized Software Projects

This paper mainly introduces the problems existing in the development process of small and medium-sized software projects in China, and analyzes the problems solved by the heavy load development method and the light load development method, and finally compares and chooses the two development methods.

A Survey: To Explore the Factors Behind Inaccuracy in Cost Effort Estimation and Providing Improvements and Suggestions

Accuracy of software cost estimation plays vital role in software development industries. Although after lot of research still this area is challenging. In my previous survey investigated the accuracy of cost effort estimation and the current estimation techniques in software industries of NCR. This paper is also analyzed 596 software development team member’s data on likert scale and conducted questionnaire survey on various organizations to investigate the reasons behind in accuracy as well assuggestions and improvements to avoid these factors. The survey presents the suggestions related to small and large software project estimation in NCR and provides evidence-based estimation improvement suggestions. The inspiration of research is to improve the effort estimation process and increase accurate estimates. Poor estimation accuracy is a big issue on running projects. Many kinds of effort estimation methods have been developed to improve the situation through algorithm based and data driven techniques to machine learning. Different kinds of methods seem to work the best in different kinds of contexts (Boehm, Abts, C Shepperd&Kadoda, 2001), so a study is needed to determine how to improve effort estimation in the context of these software companies team members.

Automatic Source Code Documentation using Code Summarization Technique of NLP

Abstract Source code documentation is an important process for software project maintenance and management. The documentation process always consumes a lot of time and effort from human experts. The software project document should be concise and clear without any ambiguity. Existing source code documentation tools like JavaDoc are very limited in the market. Also, tools can identify only the predefined methods of corresponding programming languages. In this paper, a novel system is proposed to automate the source code documentation process for C programming language using the source code summarization technique of NLP. The core component of this proposed system i.e., Software Word Usage Model (SWUM) build using Context-Free Grammars and NLP preprocessing techniques. This system can successfully generate the documentation for a C program along with predefined and user-defined methods using Natural Language Generation technique. This proposed system can document a program in two major formats; method-based, abstract level and statement-based, detailed level. The proposed system efficiency is evaluated by comparing system-generated source code documentation with an expert generated documentation. Results obtained from that comparison shows that the proposed system can give better performance for small and medium-size software projects.

A Look Over Through Survey To Analyze and Compare Accuracy of Existing Estimation Technologies on Basis of Project Size and Explore the Reasons of Inaccuracy in NCR Software Industries

Although after giving a lot of attention to software cost estimation since 1990, accuracy in effort and time estimation is still a vital and challenging area. It is very important to investigate the accuracy of cost effort estimation, the current estimation techniques, and which factors affect to adopt estimation methods in software industries of NCR. This paper analyzed 596 software development team member’s data on likert scale and conducted questionnaire survey on various organizations to investigate the above information. The survey presents the current scenario related to small and large software project estimation in NCR and provides evidence-based estimation improvement suggestions. Our survey results suggest accuracy of estimation methods and scheduling that large projects were more deviated, comparison with previous studies, satisfaction with the current project estimation methods, Expert judgement and Planning poker techniques are widely used and very few developers (46%) used theory based methods.

A NOVEL EFFORT ESTIMATION MODEL FOR SOFTWARE REQUIREMENT CHANGES DURING SOFTWARE DEVELOPMENT PHASE

Software Requirements Changes is a typical phenomenon in any software development project. Restricting incoming changes might cause user dissatisfaction and allowing too many changes might cause delay in project delivery. Moreover, the acceptance or rejection of the change requests become challenging for software project managers when these changes are occurred in Software Development Phase. Where in Software Development Phase software artifacts are not in consistent state such as: some of the class artifacts are Fully Developed, some are Half Developed, some are Major Developed, some are Minor Developed and some are Not Developed yet. However, software effort estimation and change impact analysis are the two most common techniques which might help software project managers in accepting or rejecting change requests during Software Development Phase. The aim of this research is to develop a new software change effort estimation model which helps software project manager in estimating the effort for software Requirement Changes during Software Development Phase. Thus, this research has analyzed the existing effort estimation models and change impact analysis techniques for Softwrae Development Phase from the literature and proposed a new software change effort estimation model by combining change impact analysis technique with effort estimation model. Later, the new proposed model has been evaluated by selecting four small size software projects as case selections in applying experimental approch. The experiment results show that the overall Mean Magnitude Relative Error value produced by the new proposed model is under 25%. Hence it is concluded that the new proposed model is applicable in estimating the amount of effort for requirement changes during SDP.

An Efficient Method to Generate Test Data for Software Structural Testing Using Artificial Bee Colony Optimization Algorithm

Software testing is a process for determining the quality of software system. Many small and medium-sized software projects can be manually tested. Nevertheless, due to the widespread extension of software in large-scale projects, testing them will be highly time consuming and costly. Hence, automated software testing (AST) is considered to be as a solution which can ease and simplify heavy and cumbersome tasks involved in software testing. For AST, certain data are needed through which the quality of systems can be evaluated. In this paper, an artificial bee colony (ABC) algorithm was used for solving the issue of test data generation and branch coverage criterion was used as a fitness function for optimizing the proposed solutions. For doing comparisons, seven well-known and traditional programs in the literature were used as benchmarks. The experimental results indicate that our method, on average, outperforms simulated annealing, genetic algorithm, particle swarm optimization and ant colony optimization based on the following four criteria: 99.99% average branch coverage, 99.94% success rate, 3.59 average convergence generation and 0.18[Formula: see text]ms average execution time.

Software Quality Validation for Web Applications Developed Using Geographically Distributed Human Resources

Developing web applications using Geographically Distributed Team Members has seen an increased popularity during the last years mainly because the rise of Open Source technolo-gies, fast penetration of the Internet in emerging economies, the continuous quest for reduced costs as well for the fast adoption of online platforms and services which successfully address project planning, coordination and other development tasks. This paper identifies general software process stages for both collocated and distributed development and analyses the im-pact the use of planning, management and testing online services has on the duration, cost and quality of each stage. Given that Quality Assurance is one of the most important concerns in Geographically Distributed Software Development (GDSD), the focus is on Software Qual-ity Validation.Keywords: Distributed Development, Global Software Development, Human Resources Man-agement, Project Management, Software Development Tools, Software Quality1 IntroductionDepending on both the complexity of the software product and the management model the Software Development Process may have multiple roles for its team members [1]. A typical role hierarchy is described in Figure 1. dashed lines illustrate informal inter-actions. For small software projects a team member may have multiple roles at the same time. For instance, it's rather usual for the Project Manager (PM) to perform testing op-erations as well. Also a developer could have the role of a Software Architect if the devel-opment hierarchy is not very complex.A Geographically Distributed Software De-velopment (GDSD) process happens when the team members are not collocated and share different time-zones, languages, cul-tures and legislations.Mainly as a result of distance, communica-tion, coordination and control are challenging in GDSD. Communication is reported to be one of the key processes in software devel-opment and is heavily linked to the effective-ness of coordination and control [2].From its current professional position, as the owner and manager of a software company since 2006, the author has frequently dealt with offshore development scenarios, mostly as a provider and occasionally as a customer. Based on the literature review and the au-thor's extensive experience with GDSD sce-narios, this study aims to answer the follow-ing three research questions:RQ1: Is there any set of software tools and online services that can address the commu-nication, coordination and control inconven-iences generated by the distributed approach in the GDSD?RQ2: How does the use of these software tools and online services influence the cost, duration and quality of each activity from the Software Development Process?RQ3: How is the Software Quality Validation achieved using the identified platforms for developing web applications?2 Web Applications that were analysedBetween June 2014 and May 2015, the au-thor has been involved, as part of his profes-sional activity at the company referred in this study as The Development Company - TDC, in the process of planning, developing, releasing and maintenance for 9 web applica-tions with various development team con-figurations - Table 1.* P1 is a product designed for residential associations in order to manage the dwelling expenses, utility bills, monthly consumptions and other reports. It has been built using the LAMP stack (Linux, Apache, MySQL, PHP) along with Zend Framework 2, Bootstrap and jQuery. This is an internal project of TDC which is-sued and validated the requirements, per-formed all the development operations from the beginning and is currently main-taining the application in production.* P2 is a CSR project which broadcasts live video from the Zoo in Bucharest and fea-tures a custom built Content Management System (CMS). It has been built using LAMP stack along with CodeIgniter PHP framework. TDC has exclusively man-aged all the software development stages. …

A Compliance Analysis of Agile Methodologies with the ISO/IEC 29110 Project Management Process

Software process standards (e.g. ISO/IEC 12207, ISO/IEC 15504) and models (e.g. CMMI) provide a set of best practices and guidelines for improving the quality of the software process and products resulting from that process. However, they do not prescribe a particular software development methodology (i.e. RUP, MSF), and thus software development teams face a compliance problem between the selected development methodology and a pursued particular standard or model. In this research, the particular issue of compliance of Agile Software Development Methodologies (SCRUM, XP, and UPEDU) and the new ISO/IEC 29110 standard is studied. Because the new standard is focused on the software process in very small software development companies or small software project teams in the range from 1 to 25 people, and the Agile Software Development Methodologies (ASDMs) are primarily for same targets, this study is important. The ISO/IEC 29110 standard contains two processes: Project Management and Software Implementation. This study is focused on the first process. The main findings indicate that the UPEDU and SCRUM methodologies present and high compliance level with the ISO/IEC 29110 Project Management process, while XP has a moderate level. Thus, software developer teams interested in achieving compliance with the ISO/IEC 29110 Project Management process can count with two ASDMs. However, a full compliance study (with both Project Management and Software Implementation) is still missing.

Failure factors of small software projects at a global outsourcing marketplace

The presented study aims at a better understanding of when and why small-scale software projects at a global outsourcing marketplace fail. The analysis is based on a data set of 785,325projects/tasks completed at vWorker.com. A binary logistic regression model relying solely on information known at the time of a project's start-up correctly predicted 74% of the project failures and 67% of the non-failures. The model-predicted failure probability corresponded well with the actual frequencies of failures for most levels of failure risk. The model suggests that the factors connected to the strongest reduction in the risk of failure are related to previous collaboration between the client and the provider and a low failure rate of previous projects completed by the provider. We found the characteristics of the client to be almost as important as those of the provider in explaining project failures and that the risk of project failure increased with an increased client emphasis on low price and with an increased project size. The identified relationships seem to be reasonable stable across the studied project size categories.

Estimation of the New Agile XP Process Model for Medium-Scale Projects Using Industrial Case Studies

Agile is one of the terms with which software professionals are quite familiar. Agile models promote fast development to develop high quality software. XP process model is one of the most widely used and most documented agile models. XP model is meant for small-scale projects. Since XP model is a good model, therefore there is need of its extension for the development of medium and large-scale projects. XP model has certain drawbacks such as weak documentation and poor performance while adapting it for the development of medium and large-scale projects having large teams. A new XP model is proposed in this paper to cater the needs of software development companies for medium-scale projects having large teams. This research may prove to be step forward for adaptation of the proposed new XP model for the development of large-scale projects. Two independent industrial case studies are conducted to validate the proposed new XP model handling for small and medium scale software projects, one case study for each type of project.

A Comparative Study of Two Fuzzy Logic Models for Software Development Effort Estimation

Software development effort estimation (SDEE) has been the focus of research in recent years. No single software development estimation technique is best for all situations and linear regression (LR) has frequently been used for both small and industrial software projects. Fuzzy logic (FL) has been applied as an alternative technique to SDEE using a Mamdani Model. In order to compare the estimation accuracy of the Mamdani and Takagi-Sugeno fuzzy systems with that of an LR model, a sample of small projects was used to generate two FL models and an LR equation. Then the FL models and the LR equation were validated by estimating the effort of projects elaborated by other developers. This latter group of projects was subdivided into projects with Effort<100 and Effort ≥100 (as it has been demonstrated that the estimation accuracy depends on the effort, which is an amount of time in human-hours). The results showed that the Takagi-Sugeno fuzzy system was more accurate than the Mamdani system and the LR model for SDEE of projects with Effort 100. It can be concluded that a Takagi-Sugeno fuzzy system can be useful for estimating the effort of projects with Effort≥100 when they have been individually developed on a disciplined process.

A UML Approximation of Three Chidamber-Kemerer Metrics and Their Ability to Predict Faulty Code across Software Projects

Design-complexity metrics, while measured from the code, have shown to be good predictors of fault-prone object-oriented programs. Some of the most often used metrics are the Chidamber and Kemerer metrics (CK). This paper discusses how to make early predictions of fault-prone object-oriented classes, using a UML approximation of three CK metrics. First, we present a simple approach to approximate Weighted Methods per Class (WMC), Response For Class (RFC) and Coupling Between Objects (CBO) CK metrics using UML collaboration diagrams. Then, we study the application of two data normalization techniques. Such study has a twofold purpose: to decrease the error approximation in measuring the mentioned CK metrics from UML diagrams, and to obtain a more similar data distribution of these metrics among software projects so that better prediction results are obtained when using the same prediction model across different software projects. Finally, we construct three prediction models with the source code of a package of an open source software project (Mylyn from Eclipse), and we test them with several other packages and three different small size software projects, using their UML and code metrics for comparison. The results of our empirical study lead us to conclude that the proposed UML RFC and UML CBO metrics can predict fault-proneness of code almost with the same accuracy as their respective code metrics do. The elimination of outliers and the normalization procedure used were of great utility, not only for enabling our UML metrics to predict fault-proneness of code using a code-based prediction model but also for improving the prediction results of our models across different software packages and projects.

Online Reverse Auctions for Outsourcing Small Software Projects: Determinants of Vendor Selection

Online service marketplaces have enabled small firms to efficiently outsource small-scale IT projects by providing reduced transaction costs and increased competition among bidders. This paper presents an explanatory study that examines the vendor selection process using data from a large online service marketplace. Theories from production economics, resource dependency, and software risk management are applied in a small-firm context. The empirical results suggest that a client’s utility function increases with a lower bid price, shorter lead-time, higher vendor average feedback rating, higher degree of vendor experience, and domestic (U.S.) location. There was not sufficient evidence to support strategically differentiated vendor selection behavior with respect to project value (or price). This study’s findings help understand patterns of bid evaluation in online auctions, where a large percentage of transactions consist of small IT projects.

Can k-NN imputation improve the performance of C4.5 with small software project data sets? A comparative evaluation

Missing data is a widespread problem that can affect the ability to use data to construct effective prediction systems. We investigate a common machine learning technique that can tolerate missing values, namely C4.5, to predict cost using six real world software project databases. We analyze the predictive performance after using the k-NN missing data imputation technique to see if it is better to tolerate missing data or to try to impute missing values and then apply the C4.5 algorithm. For the investigation, we simulated three missingness mechanisms, three missing data patterns, and five missing data percentages. We found that the k-NN imputation can improve the prediction accuracy of C4.5. At the same time, both C4.5 and k-NN are little affected by the missingness mechanism, but that the missing data pattern and the missing data percentage have a strong negative impact upon prediction (or imputation) accuracy particularly if the missing data percentage exceeds 40%.

A new imputation method for small software project data sets

Effort prediction is a very important issue for software project management. Historical project data sets are frequently used to support such prediction. But missing data are often contained in these data sets and this makes prediction more difficult. One common practice is to ignore the cases with missing data, but this makes the originally small software project database even smaller and can further decrease the accuracy of prediction. The alternative is missing data imputation. There are many imputation methods. Software data sets are frequently characterised by their small size but unfortunately sophisticated imputation methods prefer larger data sets. For this reason we explore using simple methods to impute missing data in small project effort data sets. We propose a class mean imputation (CMI) method based on the k-NN hot deck imputation method (MINI) to impute both continuous and nominal missing data in small data sets. We use an incremental approach to increase the variance of population. To evaluate MINI (and k-NN and CMI methods as benchmarks) we use data sets with 50 cases and 100 cases sampled from a larger industrial data set with 10%, 15%, 20% and 30% missing data percentages respectively. We also simulate Missing Completely at Random (MCAR) and Missing at Random (MAR) missingness mechanisms. The results suggest that the MINI method outperforms both CMI and the k-NN methods. We conclude that this new imputation technique can be used to impute missing values in small data sets.

Wearing two hats: analyst-managers for small software projects

With the evolution of software development, companies have changed their project methodology and are asking IT employees to evolve with them. The business analyst talks to users, ironing out the details of what they wanted and balancing that wish list against what an IT system could economically or practically deliver. The business analyst also ensures that users develop business processes to support the software. The actual task of writing the software falls to a project manager and his team of software developers. Today, companies commonly ask IT managers to assume the business analyst role in addition to their duties as project manager, especially in small projects. This article discusses how IT managers can successfully navigate the thin line between business analyst and project manager.

A Software Development Process for Small Projects

We have designed a software development process that is suitable for small software projects, of the type typically performed by postgraduate students. The aim of the process is twofold, namely to put in place a quality management system which is manageable and appropriate for student projects and to provide templates and guidelines to assist students to design good software, in keeping with the current best practices for software design.

Adapting XP to complex application domains

Extreme programming (XP) works well for small software projects in not-too-complex application domains. In many of these projects the rather simple requirements engineering of XP (customers write story cards) has proved suitable. But today we are more often faced with complex application domains in which the classical XP techniques will not suffice. The tutorial shows how to adapt extreme programming to complex application domains and to demanding development tasks. We focus mainly on the requirements engineering part and show how to enhance XP with interviews, scenarios and system visions. We also integrate the management perspective into the "planning game" reconciling this perspective to users' needs. We propose a set of best-practice methods we have used in a number of industrial high-risk projects for different complex application domains.

The helical approach to software design

This paper is used to outline a relatively recent approach to the development of software products. Practical experience of employing the traditional waterfall lifecycle model, the Spiral Model and concurrent engineering approaches in both small and large (pan-European) software projects provide the foundation on which to present, discuss and propose a new lifecycle model; the Helical LifeCycle Approach. The authors of the paper distinguish between prototypes, and model and postulate the need for the formalization of a new software engineering job role which is focused around the Helical Project LifeCycle.