Forthcoming articles

International Journal of Forensic Software Engineering

International Journal of Forensic Software Engineering (IJFSE)

These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Forensic Software Engineering (9 papers in press)

Regular Issues

  • Design of Framework for Ontological Component Retrieval from Software Component Repositories   Order a copy of this article
    by Iqbaldeep Kaur 
    Abstract: Modern component retrieval approaches are based on the context and domain of the software component in addition to using various classification and retrieval techniques like keyword search, attribute based search and semantic search. This work proposes Ontological Retrieval framework for software components that makes the use of ontology for effective retrieval of components. Ontological Component Retrieval framework has been created to facilitate the users and developers to retrieve software components matching precisely to their needs. A well-framed set of queries ranging from little to specific knowledge about the component requirement has been used to validate the results
    Keywords: Ontology; Software Component; Context Search; Semantic web services; Retrieval; Novelty Ratio; Lucene-Indexed Search; Reusability.

  • Data Preprocessing Based On Missing Value and Discretization   Order a copy of this article
    by Neeta Yadav, Neelendra Badal 
    Abstract: In the real world, data is not available in the appropriate form for mining or extracting information from this. Generally, real-world data is incomplete, inconsistent and dirty so it is very necessary to process data smartly according to the requirement of the dataset. Preprocessing is one of the most crucial steps in data mining and most of the time spent in this about 60% of the time. Unprocessed data takes lots of time in mining. End-user wasted lots of time in getting the desired result. So it is very necessary to process data according to the specific dataset by applying techniques of processing and thereby it reduces the overall mining time, the end user gets the desired result more fastly. In this paper preprocessing of missing value and discretization has been done. Preprocessing of missing value handle by three techniques that is a deletion, Replacement by mean or averages, and prediction method. From these three techniques user opt the best technique for handling missing value, which gives maximum accuracy and takes less time for preprocessing. After handling the missing value, discretization is done for data reduction so it minimizes the preprocessing time.
    Keywords: Deletion; Discretization; Missing value; Prediction; Replacement by Mean.

  • Feasibility Predictability Model for Software Test Automation Projects in DevOps Setting   Order a copy of this article
    by Jayasri Angara, Srinivas Prasad, Sridevi Gutta 
    Abstract: DevOps is an outgrowth of agile practice and evolved to manage the continuous change. The goal is to shorten the project timelines, increase productivity, without impacting business and quality. Automation has become one of the key enablers for success. However, test automation gets little time. This poses a challenge to managers whether to automate the test function or not. Managers need to take swift go/no-go decision. The objective of this paper is to develop a predictability model for test automation project feasibility. Authors conducted a literature and practitioner’s survey and identified 21 key factors which determine the viability of a project. Authors surveyed 38 test automation projects and created a dataset. A custom simulation model was developed, augmenting the dataset with 23,407 more records. Authors attempted to predict the success using machine learning algorithms. Further, factor analysis was conducted to reduce the number of factors for operational simplicity.
    Keywords: DevOps; Agile Test Automation; Project Feasibility Prediction Algorithm; Machine Learning; Logistic Regression.
    DOI: 10.1504/IJFSE.2019.10023800
     
  • Smart Design for Automation System   Order a copy of this article
    by GIRISH VARMA VEGESNA, Vijaya Nagarjana Devi Duvvuri, RAVI VEMAGIRI, SOWJANYA SWATHI NAMBHATLA, MOUNIKA KAKOLLU 
    Abstract: With the successful advancements of IoT, a new innovative product idea for the successful adjustment of brightness of available home appliances according to the user requirement is modulated. This product permits the user to adjust the brightness accordingly to their required level of extent. It concentrates on reducing the flow of electricity and banking the electricity with the help of well-equipped capacitors and eye strain. Even if the user forgets to turn off the light, he can be capable to operate through his device from any remote area. This innovative product, by interacting with sensors detects the status of the home appliance and reports the up-to-date product status daily. It also proclaims the status of the storage block in the hardware, whether started/not started to the user.
    Keywords: eye strain; capacitors; handy; sensors; status; storage block.
    DOI: 10.1504/IJFSE.2019.10023806
     
  • Parameter correlation analysis and minimum volume design of a helical gear implementing PSO algorithm   Order a copy of this article
    by Edmund S. Maputi, Rajesh Arora 
    Abstract: Gears are important mechanical elements in the assembly and functionality of a machine. The application of optimisation techniques in engineering design and gear technology has increased due to the accessibility and advancement in computational resources. Furthermore, current research trends reflect a keen interest in volume minimisation of gear systems. In this research work, a helical gear volume model is investigated. Particle swarm optimisation algorithm is applied and the results are compared and validated using an analytical method and geometric modelling software. Optimal design theory is discussed including the interrelation of concepts such as design, analysis and optimisation reflecting the need to analyse the influence of parameters on objectives. Experimental runs using genetic algorithm, firefly algorithm and teaching learning-based algorithm were also performed with statistical analysis. Parameter variation studies were also performed on each variable against the objective, minimum volume. The results of this research work, shows that module, pinion tooth number and face width are important parameters for the minimisation of gear weight with 38%, 32% and 12% contribution to variation, respectively.
    Keywords: optimization; design; gear; parameters; correlation.
    DOI: 10.1504/IJFSE.2019.10023809
     
  • Requirements’ Complexity Ranking using Natural Language Processing and Complexity Class Correlation with Defect Severity   Order a copy of this article
    by Mukundan Sundararajan, Priti Srikrishnan, Kiran Nayak 
    Abstract: This paper addresses the risks to delivery schedule and product quality from non-periodic temporal detection of high severity defects in software projects. The non-periodicity in time and lack of a time boundary in detecting severe defects primarily stems from subjective scheduling of development and testing of product features. One solution is to objectively determine complexity and ranking of requirements to drive the project development and test sequence that uncovers high severity defects early in the life cycle phases. Requirements complexity is strongly correlated with defect severity as measurements show. Applying natural language processing, key words are identified in the given set of requirements, their weights measured to determine the complexity class distribution and ranking that drives the scheduling. The complexity and defect correlation-based sequencing mitigates the risks by discovery of high severity defects in a temporal saw tooth pattern providing the project team sufficient time to fix defects and mitigate the risks.
    Keywords: Requirements complexity; Requirements ranking; Requirement complexity classes; Natural language processing; NLP; Defect severity correlation; defect spatial distribution; defect temporal distribution.
    DOI: 10.1504/IJFSE.2019.10023810
     
  • ROLE OF BLOCKCHAIN ELEMENTS IN ACHIEVING SUPPLY CHAIN MANAGEMENT OBJECTIVES-AN EMPIRICAL INVESTIGATION   Order a copy of this article
    by Shelly Oberoi, SUBODH K. KESHARWANI, Madhulika P. Sarkar 
    Abstract: Evolution of blockchain has transformed the supply chain activities and it has affected the various activities of organisations. This study attempts to explore the role of blockchain elements on supply chain management. A sample of 150 suppliers from retail industry (Delhi/NCR) was taken. The data is collected using a structured questionnaire based on five-point Likert's scale using convenience sampling. Six constructs, consisting of 12 items, are being identified and hypotheses are tested using factor analysis and model fit is done using confirmatory factor analysis (CFA) and structural equation modelling (SEM). The study highlights that modern blockchain technology will affect supply chain of retail industry in positive way as it provides the features of privacy, security, scalability and immutability. Hence, blockchain models should be adopted in supply chain which will be beneficial for all the stakeholders.
    Keywords: blockchain; cryptocurrency; immutability; internet; organizations; privacy; scalability; security; supply chain; technology.
    DOI: 10.1504/IJFSE.2019.10024960
     
  • Distributed computing using SMART sensors in industrial automation framework   Order a copy of this article
    by Naveen Kumar Singa, Nilesh Jadhav, Bony Mathew 
    Abstract: Transition from single node computing to complex distributed computing environments has proven its efficiency in the computer networks especially in executing heavy computational tasks. The sensors on the shop/factory floor have become smart with additional computational capabilities compared to passive acquisition nodes. We merged these two entities and evaluated the distributed computing of PLC load on smart sensors on an industrial automation framework. We have utilised SoC (Raspberry Pi) to build a SMART sensor which has allowed us to create a distributed and scalable sensor network structure. This structure in conjunction with state-of-the-art industrial automation network improves the performance of the automation networks.
    Keywords: SMART sensor; distributed computing; cluster; industrial automation network; fog computing.
    DOI: 10.1504/IJFSE.2019.10030652
     
  • Performance analysis of data mining software with parametric changes   Order a copy of this article
    by Ercan Atagün, Irem Duzdar Argun 
    Abstract: There are significant increases in data mining software as in the data mining studies in the last years. Those increasing software are developed depending on the usage areas. Because of this reason, various parameters are supplied to the users. The parameters have the default values, but they may change with the values used by the user, and they are integrated when the algorithm is applied. In this study, supplying various parameters according to the classification parameters of the data mining software, and the effect of those parameters, which are manageable, on the success of the classification are analysed. Three different algorithms, support vector machine (SVM), K nearest neighbour (KNN) and artificial neural networks (ANN) are applied to five various data mining and the effects of manageable parameters on the classification successes are studied.
    Keywords: data mining; software; classification; parameter; performance analysis.
    DOI: 10.1504/IJFSE.2020.10031015