Phases Of Software Development Life Cycle Research Articles

Systematic Mapping Study on Security Approaches in Secure Software Engineering

In the modern digital era, software systems are extensively adapted and have become an integral component of human society. Such wide use of software systems consists of large and more critical data that inevitably needs to be secured. It is imperative to make sure that these software systems not only satisfy the users’ needs or functional requirements, but it is equally important to make sure the security of these software systems. However, recent research shows that many software development methods do not explicitly include software security measures during software development as they move from demand engineering to their final losses. Integrating software security at each stage of the software development life cycle (SDLC) has become an urgent need. Tackling software security, various methods, techniques, and models have been suggested and developed, however, only a few of them provide strong evidence for building secure software applications. The main purpose of this research is to study security measures in the context of the development of secure software (SSD) during the study of systematic mapping (SMS). Based on the inclusion and exclusion criteria, 116 studies were selected. After the data extraction from the selected 116 papers, these were classified based on the quality assessment, software security method, SDLC phases, publication venue, and SWOT analysis. The results indicate that this domain is still immature and sufficient research work needs to be carried out particularly on empirically evaluated solutions.

Enhanced Framework for Big Data Requirement Elicitation

Requirement engineering is one of the software development life cycle phases; it has been recognized as an important phase for collecting and analyzing a system’s goals. However, despite its importance, requirement engineering has several limitations such as incomplete requirements, vague requirements, lack of prioritization, and less user involvement, all of which affect requirement quality. With the emergence of big data technology, the complexity of big data, which is defined by large data volume, high velocity, and large data variety, has gradually increased, affecting the quality of big data software requirements. This study proposes a framework with four sequential phases to improve requirement engineering quality through big data software development. By integrating the proposed framework’s phases in which user requirements are collected in a complete vision using traditional requirement elicitation techniques with agile methodology and mind mapping, the collected requirements are displayed via a graphical representation using mind maps to achieve high requirement accuracy with connectivity and modifiability, enabling the accurate prioritization of requirements implemented using agile SCRUM methodology. The proposed framework improves requirement quality in big data software development, which is represented by accuracy, completeness, connectivity, and modifiability to understand the value of the collected requirements and effectively affect the quality of the implementation phase.

Big Data Systems

Big Data Systems (BDSs) are an emerging class of scalable software technologies whereby massive amounts of heterogeneous data are gathered from multiple sources, managed, analyzed (in batch, stream or hybrid fashion), and served to end-users and external applications. Such systems pose specific challenges in all phases of software development lifecycle and might become very complex by evolving data, technologies, and target value over time. Consequently, many organizations and enterprises have found it difficult to adopt BDSs. In this article, we provide insight into three major activities of software engineering in the context of BDSs as well as the choices made to tackle them regarding state-of-the-art research and industry efforts. These activities include the engineering of requirements, designing and constructing software to meet the specified requirements, and software/data quality assurance. We also disclose some open challenges of developing effective BDSs, which need attention from both researchers and practitioners.

Assessment of optimum refactoring sequence to improve the software quality of object-oriented software

Software Maintenance is the most expensive phase of Software Development Lifecycle (SDLC) and therefore requires maximum effort and time. Any modification in the source code also introduces some bad smell which may be harmful for the design of the source code. These bad smells get accumulated with every phase of the SDLC if not corrected timely, therefore, they need to be removed as early as possible with the help of refactoring which improves the internal attributes of the software without affecting the external attributes. In order to complete the software under time and budget constraints, authors have attempted to determine the optimum sequence of refactoring technique (RT) in advance so that maintenance team can directly apply them in order to remove the bad smell which further enhances the quality of the software. Authors have applied different types of Hill- Climbing algorithms. Results shows that that the Steepest-Ascent Hill-Climbing algorithm is the most promising algorithm in finding the most optimum refactoring sequence with 20% improvement in the maintainability value.

Smart Test Case Quantifier Based on Mc/Dc Coverage Criterion with Uml Sequence Diagram

Software testing is an inevitable phase in the Software Development Life Cycle (SDLC). It plays a vital role in ensuring the quality of the product. Testing can be performed on requirement, design and code. Traditional test case generation approaches majorly focuses on designing and implementation models, as a huge percentage of errors in the software are caused due to the lack of understanding in the early phases. So, it’s advisory to follow testing in the initial phase in SDLC as most of the errors/faults can be eliminated and can be prevented without disseminating to the next phase. So, testing must not be secluded to a single phase alone in SDLC. In software testing, test case generation is the fundamental and challenging part. The major purpose to design test case is to create set of tests that are valuable in validation. Due to the increase in software size, it’s not quite advisable to have the choice as manual testing to test the code as its error prone, complex and time consuming. Automation of testing would help the tester to test effectively and timely. Applications of new techniques improve the test process and cut down the tester’s effort. The proposed work presents a sandwich of black box and white box testing. Design-based testing, a black box approach is implemented, that aids in fixing the errors at initial phase. Unified Modeling Language (UML) Use case diagram, Activity diagram and Sequence Diagram are considered for designing. The code-based generation of test cases, is a white box testing approach, test coverage criteria Modified Condition/Decision Condition (MC/DC) has been used to ensure the maximum coverage of the code during testing phase for generating the test cases. The objective of this paper is to automate the generation of minimal number of test cases required to test a system with maximum coverage by removing the redundant test cases using MC/DC criterion. This present an idea to the beginners of the testing about minimal test cases they necessitate for testing their system.

Change Impact Analysis using Python for Java Applications

Software Applications needs to be changed constantly as per the requirements of the user or the client. This phase is known as the maintenance phase of a software application. On an average, the cost of software maintenance is more than 50% of all Software Development Life Cycle phases. The main purpose of software maintenance is to modify and update software application after delivery in order to correct faults, enhance the functionality or to improve performance. When the change request (CR) is received from the client the developers have to work upon the request. If the Change Request requires any modification, the application developers have to identify the risk of modifying the program or application before making the actual change. But there is a high chance of making errors in modifying the existing software. Change Impact analysis (CIA) is one of the error prevention technique. It is the process to find the effect of a change in a software application before the changes are made. By equipping developers with automated CIA tools to identify the risk of modifying the application we can minimize the errors. Impact analysis can be done based on the change request. This paper aims at developing a change impact analysis tool which can be used by the developer during the software maintenance phase. This proposed tool -Strategic Dependency Tracker (SDepTrac) helps the programmer/ developer to know “What part of the program (of a Java application) is impacted if a change is to be made to a particular variable / method / class?” with more accuracy compared to the existing tools. It displays the number of lines affected, classes and methods which are going to be affected, if we perform the requested change by considering the data dependency, control dependency and the semantic dependency. Thus the tool helps the developer to identify the impact set and minimizes the human errors and also saves time during the Maintenance phase.

Software defect prediction: A multi-criteria decision-making approach

Failure of software systems as a result of software testing is very much rampant as modern software systems are large and complex. Software testing which is an integral part of the software development life cycle (SDLC), consumes both human and capital resources. As such, software defect prediction (SDP) mechanisms are deployed to strengthen the software testing phase in SDLC by predicting defect prone modules or components in software systems. Machine learning models are used for developing the SDP models with great successes achieved. Moreover, some studies have highlighted that a combination of machine learning models as a form of an ensemble is better than single SDP models in terms of prediction accuracy. However, the efficiency of machine learning models can change with diverse predictive evaluation metrics. Thus, more studies are needed to establish the effectiveness of ensemble SDP models over single SDP models. This study proposes the deployment of Multi-Criteria Decision Method (MCDM) techniques to rank machine learning models. Analytic Network Process (ANP) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) which are types of MCDM techniques are deployed on 9 machine learning models with 11 performance evaluation metrics and 11 software defects datasets. The experimental results showed that ensemble SDP models are best appropriate SDP models as Boosted SMO and Boosted PART ranked highest for each of the MCDM techniques. Besides, the experimental results also validated the stand of not considering accuracy as the only performance evaluation metrics for SDP models. Conclusively, more performance metrics other than predictive accuracy should be considered when ranking and evaluating machine learning models.
 Keywords: Ensemble; Multi-Criteria Decision Method; Software Defect Prediction

Towards recent developments in the methods, metrics and datasets of software fault prediction

The world of software systems is amplified with the changing environment magnifying the demand for quality software. Software fault prediction is a requisite activity ensuring the development of economic, efficient and quality software. It is the procedure for the development of models which help to identify faults in modules during early phases of software development lifecycle. Software fault prediction is one of the most prevalent research disciplines. The existing study in this domain includes numerous modelling techniques and software metrics for the early predictions of software faults. This paper aims to explore some of the prominent studies for software fault prediction in the existing literature. In this paper, software fault prediction papers since 1990 to 2017 are investigated. The paper includes the analysis of the studies having empirical validation and a good source of publication. The paper reflects the methods, metrics, and datasets available in the literature for software fault prediction. In addition, the modelling techniques based on traditional and computational intelligence-based methods are also reviewed. This paper is an endeavour to assemble the existing techniques and metrics of software fault prediction with a motive to assist researchers for easy evaluation of suitable metrics for their own research scenarios.

Towards recent developments in the methods, metrics and datasets of software fault prediction

The world of software systems is amplified with the changing environment magnifying the demand for quality software. Software fault prediction is a requisite activity ensuring the development of economic, efficient and quality software. It is the procedure for the development of models which help to identify faults in modules during early phases of software development lifecycle. Software fault prediction is one of the most prevalent research disciplines. The existing study in this domain includes numerous modelling techniques and software metrics for the early predictions of software faults. This paper aims to explore some of the prominent studies for software fault prediction in the existing literature. In this paper, software fault prediction papers since 1990 to 2017 are investigated. The paper includes the analysis of the studies having empirical validation and a good source of publication. The paper reflects the methods, metrics, and datasets available in the literature for software fault prediction. In addition, the modelling techniques based on traditional and computational intelligence-based methods are also reviewed. This paper is an endeavour to assemble the existing techniques and metrics of software fault prediction with a motive to assist researchers for easy evaluation of suitable metrics for their own research scenarios.

A Maturity Model for Secure Software Design: A Multivocal Study

Security is one of the most important software quality attributes. Software security is about designing and developing secure software that does not allow the integrity, confidentiality, and availability of its code, data, or service to be compromised. Organizations tend to consider security as an afterthought, and they continue to suffer from security risks. Developing secure software requires taking security into consideration in all phases of the Software Development Life Cycle (SDLC). Several approaches have been developed to improve software quality, such as Capability Maturity Model Integration (CMMI). However, software security issues have not been addressed in a proper manner and incorporating security practices into the SDLC remains a challenge. The objective of this paper is to develop a framework to improve the process of designing secure products in software development organizations. To achieve this objective, a Multivocal Literature Review (MLR) was conducted to identify the relevant studies in both the formal and grey literature. A total of 38 primary studies were identified, and available evidence was synthesized into 8 knowledge areas and 65 best practices to build a Secure Software Design Maturity Model (SSDMM). The framework was developed based on the structure of CMMI v2.0 and evaluated through case studies in real-world environments. The case study results indicate that SSDMM is useful in measuring the maturity level of an organization for the secure design phase of SDLC. SSDMM will assist organizations in evaluating and improving their software design security practices. It will also provide a foundation for researchers to develop new software security approaches.

Software Development Lifecycle for Energy Efficiency

Motivation: In modern it systems, the increasing demand for computational power is tightly coupled with ever higher energy consumption. Traditionally, energy efficiency research has focused on reducing energy consumption at the hardware level. Nevertheless, the software itself provides numerous opportunities for improving energy efficiency. Goal: Given that energy efficiency for it systems is a rising concern, we investigate existing work in the area of energy-aware software development and identify open research challenges. Our goal is to reveal limitations, features, and tradeoffs regarding energy-performance for software development and provide insights on existing approaches, tools, and techniques for energy-efficient programming. Method: We analyze and categorize research work mostly extracted from top-tier conferences and journals concerning energy efficiency across the software development lifecycle phases. Results: Our analysis shows that related work in this area has focused mainly on the implementation and verification phases of the software development lifecycle. Existing work shows that the use of parallel and approximate programming, source code analyzers, efficient data structures, coding practices, and specific programming languages can significantly increase energy efficiency. Moreover, the utilization of energy monitoring tools and benchmarks can provide insights for the software practitioners and raise energy-awareness during the development phase.

Adaptive Concept Map Approach for Software Requirements Validation

Requirements validation is one of the most significant and critical parts of the requirements engineering. This activity ensures that the set of requirements is accurate, right, complete, and consistent. Requirements validation is considered as the key activity because mistakes found in a software requirements document can lead to extensive rework costs when they are discovered either during development or after the system is in service. There are some commonly used bases to validate user requirements such as: Natural language, Design description languages, Graphical notations and Mathematical specification languages. Whereas the graphical notations are the most suitable means to be used in software requirements validation because it is easy to understand, and it can be easily created by analyst and time took. Therefore, this paper adopts the map concept which is a graphical technique for discovering the hidden flaws in software requirements in the early phases of software development lifecycle.

Analysis of Test Management, Functional and Load Testing Tools

Software testing is one of the most important phases in software development lifecycle (SDLC). Software testing is the process of evaluating the software product with the intent to find whether it satisfies the user requirements or not. It involves identifying bug, error, or defect in a software product without correcting it. There are various automated tools, which help as to test the software products with accuracy. This paper analyses some of the test management, functional and load testing tools.

Birds of a Feather Gel Together: Impact of Team Homogeneity on Software Quality and Team Productivity

Even though a lot of projects fail due to social issues or personality conflicts, only a small number of empirical studies have been conducted to quantitatively assess the impact of individual personality attributes on the software being developed and the team developing that software. The goal of this paper is to quantify the abstract notion of team homogeneity and to measure its impact on software quality and team productivity. A metric called team homogeneity index (THI) is proposed for this purpose. The six-step process of calculating the THI of a software development team is described and illustrated with the help of an example. Finally, the utility of THI is assessed by conducting a controlled experiment in two different phases of the software development life cycle (SDLC), i.e., implementation and testing. The results reveal that, during the implementation phase, teams with greater THI values were noticeably more productive and produced better quality code. Similarly, during the testing phase, teams with higher values of THI tested more features and wrote better quality test cases. Therefore, the evidence obtained so far suggests that the newly proposed metric, THI, appears to be useful in predicting the quality of software and the productivity of software development teams. Future work includes determining the weights of the five traits using input from the software industry and replication of this empirical study on different phases of SDLC with software practitioners to validate our findings.

Prioritising test cases by collaborating artefacts of software development life cycle

Test case prioritisation reorders test cases based on faulty module detection capability to detect maximum faults by minimum execution. Since 40% budget is allocated for software testing, prioritisation can reduce that budget by early fault detection. In software development life cycle (SDLC), faults are propagated to the connected phase and continuing until final release, because every phase depends on its previous phase's outcomes. This motivation derived prioritisation approach by focusing collaboration of different SDLC phases named requirements, design and code. Since, each of the SDLC phase has its own impact on test case, unique priority constants are assigned to every phase, which are used for constructing final priorities. The proposed framework was experimented on different projects, and results have been compared to several prominent schemes considering individual phase of SDLC. On average, proposed collaborative approach performs 22.77% and 29.01% better than individually requirements and source code based prioritisation techniques respectively.

Early software defect prediction: A systematic map and review

ContextSoftware defect prediction is a trending research topic, and a wide variety of the published papers focus on coding phase or after. A limited number of papers, however, includes the prior (early) phases of the software development lifecycle (SDLC). ObjectiveThe goal of this study is to obtain a general view of the characteristics and usefulness of Early Software Defect Prediction (ESDP) models reported in scientific literature. MethodA systematic mapping and systematic literature review study has been conducted. We searched for the studies reported between 2000 and 2016. We reviewed 52 studies and analyzed the trend and demographics, maturity of state-of-research, in-depth characteristics, success and benefits of ESDP models. ResultsWe found that categorical models that rely on requirement and design phase metrics, and few continuous models including metrics from requirements phase are very successful. We also found that most studies reported qualitative benefits of using ESDP models. ConclusionWe have highlighted the most preferred prediction methods, metrics, datasets and performance evaluation methods, as well as the addressed SDLC phases. We expect the results will be useful for software teams by guiding them to use early predictors effectively in practice, and for researchers in directing their future efforts.

Sprint Backlog Estimating and Planning Using Planning Poker Technique in Agile Scrum Framework

Today and during the recent years, Agile Product Development Framework has been common used framework for software development particularly, Scrum is the most widely known methodology under the umbrella of agile, including many sprints to manage the development process of software. One of the most important terms in each scrum sprint is the planning and estimation phase. Many techniques to plan the sprint backlog and specify the required time and resources to perform each single backlog item. As some of planning techniques as Planning Poker, T-Shirt Sizes and Relative Mass Valuation. Planning Poker is considered an effective and powerful technique according to its algorithm of work which evolves the whole team members estimation to user stories backlog. Planning Poker gets the backlog items and input and gives the well estimated tasks and output. Planning Poker helps the product owners and scrum team to successfully estimate the work that ensures working Relative Value Points Return On Investment goals specified since the beginning of the software development life cycle phases. The importance of Planning Poker is in giving the optimum estimation with consideration to high priority backlog items and user stories.

Development of a Bayesian belief network model for software reliability quantification of digital protection systems in nuclear power plants

As the instrumentation and control (I&C) systems in nuclear power plants (NPPs) have been replaced with digital-based systems, the need has emerged to not only establish a basis for incorporating software behavior into digital I&C system reliability models, but also to quantify the software reliability used in NPP digital protection systems. Therefore, a Bayesian belief network (BBN) model which estimates the number of faults in a software considering its software development life cycle (SDLC) is developed in this study. The model structure and parameters are established based on the information applicable to safety-related systems and expert elicitation. The evidence used in the model was collected from three stages of expert elicitation. To assess the feasibility of using BBN in NPP digital protection software reliability quantification, the BBN model was applied to the Integrated Digital Protection System–Reactor Protection System and estimated the number of defects at each SDLC phase and further assessed the software failure probability. The developed BBN model can be employed to estimate the reliability of deployed safety-related NPP software and such results can be used to evaluate the quality of the digital I&C systems in addition to estimating the potential reactor risk due to software failure.

Measuring Security

The researchers have been focusing on embedding security from the early phases of software development lifecycle. They have researched and innovated a field of Security Engineering where security concerns are embedded during requirement, design, and testing phases of software development. Efforts were made in developing methods, methodologies, and tools to handle security issues. Various methods are present in the literature for eliciting, analyzing and prioritizing the security requirements. During the design phase based on prioritized requirements, environment parameters and attribute a suitable security algorithm mainly cryptography algorithms are identified. Then a question arises how to test the effectiveness of chosen algorithm? Therefore, as an answer to the issue in this paper, a process for Security Testing is presented that evaluates the selected security algorithms. Evaluation is done by generating the test scenarios for functionalities using sequence diagram representing the threats at vulnerable points. Then, checking the mitigation of potential threats at identified vulnerable points. A security index is generated which shows the effectiveness of deployed/ chosen security algorithm. The process ends with the generation of a test report depicting the testing summary. For a clear understanding of the process, the proposal is illustrated with a case study of the cloud storage as a service model.

A study of SDLC to develop well engineered software

Software Development Life Cycle (SDLC) is an important concept used in software engineering to describe a procedure for planning, creating, coding, testing and implementation of user requirement specification. Software development life cycle applies to a range of hardware and software configurations. SDLC is step by step process for creating quality software for users. It involves different phases that are followed one after one, that are essential for software engineers such as planning, analysis, design, coding, testing and implementation. In the early years, hardware was costly and software relatively cheap. In digital era, hardware is cheap and software is expensive. So the costs of hardware and software have been reversed due to increased demand of well engineered software products. This paper includes different phases of SDLC, software quality, qualities of well engineered software and factors affecting software quality. Key words: SDLC, Phases, Software Quality, Factors