Software Engineering Applications Research Articles

A Review on Graph Theory in Network and Artificial Intelligence

The Mathematics field assumes crucial function in different fields. One of the significant territories in Mathematics is Graph theory which isutilized in basic models. We give a study of Graph theory utilized in computer sciences. The study comprises of a portrayal of specific themes from the Graph theory of the zone of Computer science where they are utilized. In any case, for each portrayed hypothesis we show the fields where it is utilized. For example in demonstrating and looking through Internet, in Computer science vision, design acknowledgment, information mining, multiprocessor frameworks, factual information bases, and in a few different regions. This paper gives a overview of the application of Graph theory in heterogeneous fields somewhat however chiefly centers around the software engineering applications that utilizations Graph theory ideas. Computer Science and Artificial Intelligence applications and an overview has been presented in this article.

Knowledge Intensive Software Engineering Applications

The use of Information and Communication Technologies (ICTs)  has become a competitive strategy that allows organizations to position themselves within their market of action. In addition, the evolution, advancement and use of ICTs within any type of organization have created new domains of interest. In this context, Knowledge-intensive software engineering applications are becoming crucial in organizations to support their performance. Knowledge-based technologies provide a consistent and reliable basis to face the challenges for organization, manipulation and visualization of the data and knowledge, playing a crucial role as the technological basis of the development of a large number of information systems. In software engineering, it involves the integration of various knowledge sources that are in constant change. Knowledge-intensive software applications are becoming more significant because the domains of many software applications are inherently knowledge-intensive and this knowledge is often not explicitly dealt with in software development. This impedes maintenance and reuse. Moreover, it is generally known that developing software requires expertise and experience, which are currently also implicit and could be made more tangible and reusable using knowledge-based or related techniques. Furthermore, organizations have recognized that the software engineering applications are an optimal way for providing solutions, because it is a file that is constantly evolving due to the new challenges. Examples of approaches that are directly related to this tendency are data analysis, software architectures, knowledge engineering, ontologies, conceptual modelling, domain analysis and domain engineering, business rules, workflow management, human and cultural factors, to mention but a few. Therefore, tools and techniques are necessary to capture and process knowledge in order to facilitate subsequent development efforts, especially in the domain of software engineering.  

Communication architecture based on IoT technology to control and monitor pets feeding

Technology is currently a significant benchmark in any application area; science and technology have permitted the invention of tools and devices that simplify daily activities by developing software engineering applications that provide automated solutions. In this sense, this work proposes two architectures that allow communication between the electronic device and the mobile application remotely, using the GSM/GPRS communication services and the Twitter social network. This development aims to control dogs' feeding adequately and healthily, providing the ration of food a dog needs according to the daily energy requirements. A nutritional assessment has also been performed considering different factors such as the size, breed, and weight of the dog to calculate the daily ration of healthy and balanced food according to daily energy requirements. Essentially, the electronic device consists of two parts: on the one hand, the electronic design is formed with an Arduino board, a Sim900 module to send and receive text messages, and the ESP8266 Wi-Fi serial transceiver module, which allows establishing the internet connection to receive the tweet that users post, both modules permit remote communication with the device using the Arduino board. On the other hand, the mobile application developed on Android uses a standard design according to the Google material design guidelines, allowing the owner to feed, schedule the feeding, review the dog's food history, and receive alerts when the food is going to be finished.

Isolation Forest Wrapper Approach for Feature Selection in Software Defect Prediction

Software defect prediction is one of the hot research topics in the software engineering application. The performance of predictor largely depends on the quality of dataset used for learning the predictor. High dimensionality is a noteworthy characteristic of software defect dataset, which has some side-effect on the predictor building using data mining or machine learning algorithm. Feature selection, being an effective measure of dimensionality reduction, uses the optimal feature subset to represent the entire feature space and alleviate the dimensionality curse problem. In this paper, a wrapper feature selection approach applying genetic algorithm as a search strategy to find the optimal feature subset is firstly introduced. Secondly, an improved isolation forest based defect prediction method is proposed. The exploring experiments on 5 real NASA software defect datasets demonstrate the proposed method can improve the defect prediction performance to some extent and proves the positive effect of feature selection in SDP application.

Validating class integration test order generation systems with Metamorphic Testing

Previous studies proposed different kinds of approaches for class integration test order generation, and corresponding systems can be implemented based on these approaches. Such class integration test order generation systems can facilitate the process of software integration testing if they are implemented correctly. However, a test oracle problem exists in the class integration test order generation systems. Since these approaches for class integration test order generation normally deliver a local optimum rather than a global optimum, there are no practically feasible ways to validate their generated class integration test orders, that is, these implementation systems are untestable. To address the test oracle problem, we apply Metamorphic Testing (MT) to validate class integration test order generation systems. Metamorphic Relations (MRs), which are the key components of MT, reason about relations between test outputs of a system. Five effective MRs are developed to ensure the quality of the class integration test order generation systems. In these proposed MRs, follow-up test inputs are generated by modifying classes or class dependencies in the source test inputs while some characteristics of the source test outputs are preserved, for example, the same class integration test order or the equal stubbing cost. Faults can be detected in systems if an individual MR is violated for certain tests. Failure detection of MT has been successfully demonstrated in empirical experiments on three systems implementing different typical class integration test order generation approaches. More than 84% of faulty programs can be detected by all MRs, for three class integration test order generation systems under investigation. The experimental results show that the proposed MRs are able to systematically and effectively detect faults in class integration test order generation systems. This study explores a new application domain in MT and further extends its applications in Software Engineering.

Research on Civil Aircraft Structural Strength Material Performance Database Based on SpringBoot

According to various materials used in civil aircraft structural strength analysis, and applying software engineering ideas and object-oriented programming methods, a set of civil aircraft structural strength material performance database system based on SpringBoot was developed. The scope of the material library management and the input data standards are initially defined, through the analysis of civil aircraft material data, the metal, composite materials and layup data are classified and managed respectively. The material library used for finite element calculation and strength inspection also provides the export function of Nastran material card, it has a certain reference value for highly-managed civil aircraft depots, and can achieve work collaboration and improve work efficiency.

Software engineering and advanced applications conference 2019 – selected papers

Software Engineering and Advanced Applications (SEAA) is a long-standing international forum for researchers, practitioners, and students to present and discuss the latest innovations, trends, experiences, and concerns in the field of Software Engineering and Advanced Applications in information technology for software-intensive systems. In this special issue, we present a selection of papers which show the current trends in software engineering – improved systematic reviews, deep learning and cloud computing.

Fundamental results for learning deterministic extended finite state machines from queries

Regular language inference, initiated by Angluin, has many developments, including applications in software engineering and testing. However, the capability of finite automata to model the system data is quite limited and, in many cases, extended finite state machine formalisms, that combine the system control with data structures, are used instead. The application of Angluin-style inference algorithms to extended state machines would involve constructing a minimal deterministic extended finite state machine consistent with a deterministic 3-valued deterministic finite automaton. In addition to the usual, accepting and rejecting, states of finite automaton, a 3-valued deterministic finite automaton may have “don't care” states; the sequences of inputs that reach such states may be considered as accepted or rejected, as is convenient. The aforementioned construction reduces to finding a minimal deterministic finite automaton consistent with a 3-valued deterministic finite automaton, that preserves the deterministic nature of the extended model that also handles the data structure associated with it. This paper investigates fundamental properties of extended finite state machines in relation to Angluin's language inference problem and provides an inference algorithm for such models.

DKL: an efficient algorithm for learning deterministic Kripke structures

There has been a recent growth of interest in software engineering community to use grammatical inference, aka automaton learning, in software engineering applications. This is primarily due to the reason that capacity of underlying hardware resources has improved significantly over the last years; which has enabled the use of this approach beyond the toy examples in a greater frequency. In this paper, we present a new grammatical inference algorithm, DKL, to learn deterministic Kripke structures. The DKL is only the second of its kind for incremental learning of deterministic Kripke structures. Earlier, IKL algorithm was introduced for learning deterministic Kripke structures but it often constructs a hypothesis much larger in state size than the target Kripke structure. This problem is known as state-space explosion and it primarily occurs due to the sub-direct product construction used in the IKL design, which in turn affects time efficiency of IKL; especially when it is used for practical applications of software engineering. The DKL algorithm is designed to resolve the problem of state-space explosion. We give a proof of correctness and termination of the DKL algorithm. We also compared the performance of DKL with IKL by implementing an evaluation framework. Our results show that DKL is more efficient in terms of time in reaching the target automaton than the IKL algorithm and is not prone to the problem of state-space explosion.

The ‘as code’ activities: development anti-patterns for infrastructure as code

Context: The 'as code' suffix in infrastructure as code (IaC) refers to applying software engineering activities, such as version control, to maintain IaC scripts. Without the application of these activities, defects that can have serious consequences may be introduced in IaC scripts. A systematic investigation of the development anti-patterns for IaC scripts can guide practitioners in identifying activities to avoid defects in IaC scripts. Development anti-patterns are recurring development activities that relate with defective IaC scripts. Goal: The goal of this paper is to help practitioners improve the quality of infrastructure as code (IaC) scripts by identifying development activities that relate with defective IaC scripts. Methodology: We identify development anti-patterns by adopting a mixed-methods approach, where we apply quantitative analysis with 2,138 open source IaC scripts and conduct a survey with 51 practitioners. Findings: We observe five development activities to be related with defective IaC scripts from our quantitative analysis. We identify five development anti-patterns namely, 'boss is not around', 'many cooks spoil', 'minors are spoiler', 'silos', and 'unfocused contribution'. Conclusion: Our identified development anti-patterns suggest the importance of 'as code' activities in IaC because these activities are related to quality of IaC scripts.

An assessment of practitioners approaches to forecasting in the presence of changepoints

AbstractA common challenge in time series is to forecast data that suffer from structural breaks or changepoints which complicate modeling. If we naively forecast using one model for the whole data, the model will be incorrect, and thus, our forecast error will be large. There are two common practices to account for these changepoints when the goal is forecasting: (1) preprocess the data to identify the changepoints, incorporating them as dummy variables in modeling the whole data, and (2) include the changepoint estimation into the model and forecast using the model fit to the last segment. This article examines these two practices, using the computationally exact Pruned Exact Linear Time (PELT) algorithm for changepoint detection, comparing and contrasting them in the context of an important Software Engineering application.

(AIAM2019) Artificial Intelligence in Software Engineering and inverse: Review

ABSTRACT Artificial Intelligence (AI) and Software Engineering are considered as significant fields to solve various problems. However, there are weaknesses in certain problem-solving in each field. Thus, this paper is a broad-based review of using artificial intelligence (AI) to improve software engineering (SE), and vice versa. As well as it intends to review the techniques developed in artificial intelligence from the standpoint of their application in software engineering. The aim of this review is highlighted in how the previous study benefited from incorporating the advantages of both fields. The researchers and practitioners on AI and SE belong to a wide range of audiences from the domains of optimization, engineering, data mining, clustering, etc., who will benefit from this study and areas for potential future research.

CodeGRU: Context-aware deep learning with gated recurrent unit for source code modeling

Recently deep learning based Natural Language Processing (NLP) models have shown great potential in the modeling of source code. However, a major limitation of these approaches is that they take source code as simple tokens of text and ignore its contextual, syntactical and structural dependencies. In this work, we present CodeGRU, a gated recurrent unit based source code language model that is capable of capturing source code's contextual, syntactical and structural dependencies. We introduce a novel approach which can capture the source code context by leveraging the source code token types. Further, we adopt a novel approach which can learn variable size context by taking into account source code's syntax, and structural information. We evaluate CodeGRU with real-world data set and it shows that CodeGRU outperforms the state-of-the-art language models and help reduce the vocabulary size up to 24.93\%. Unlike previous works, we tested CodeGRU with an independent test set which suggests that our methodology does not requisite the source code comes from the same domain as training data while providing suggestions. We further evaluate CodeGRU with two software engineering applications: source code suggestion, and source code completion. Our experiment confirms that the source code's contextual information can be vital and can help improve the software language models. The extensive evaluation of CodeGRU shows that it outperforms the state-of-the-art models. The results further suggest that the proposed approach can help reduce the vocabulary size and is of practical use for software developers.

Guest Editorial: Software Engineering Applications to Solve Organisations Issues

Guest Editorial: Software Engineering Applications to Solve Organisations Issues

Integrating Software Engineering Processes in the Development of Efficient Intrusion Detection Systems in Wireless Sensor Networks.

Applying Software Engineering (SE) processes is vital to critical and complex systems including security and networking systems. Nowadays, Wireless Sensor Networks (WSNs) and their applications are found in many military and civilian systems which make them attractive to security attackers. The increasing risks and system vulnerabilities of WSNs have encouraged researchers and developers to propose many security solutions including software-based Intrusion Detection Systems (IDSs). The main drawbacks of current IDSs are due to the lack of clear, structured software development processes. Unfortunately, a substantial gap has been observed between WSN and SE research communities. Integrating SE and WSNs is an emerging topic that will be expanded as technology evolves and spreads in all life aspects. Consequently, this paper highlighted the importance of Requirement Engineering, Software Design, and Testing when developing IDSs for WSNs. Three software IDS designs were proposed in this study: Scheduling, Broadcast, and Watchdog designs. The three designs were compared in terms of consumed energy and network lifetime. Although the same IDS approach was used, but, by highlighting the design phase and implementing different designs, the network lifetime was increased by 73.6% and the consumed energy was reduced by 20% in some of the designs. This is a clear indication of how following a proper SE process could affect the performance of the IDS in WSN. Moreover, conclusions were drawn in regard to applying software engineering processes to IDSs to deliver the required functionalities, with respect to operational constraints, with an improved performance, accuracy and reliability.

IGRINS Slit-viewing Camera Software

We have developed observation control software for the Immersion GRating INfrared Spectrometer (IGRINS) slit-viewing camera module, which maintains the position of an astronomical target on the spectroscopic slit. It is composed of several packages that monitor and control the system, acquire the images, and compensate for the tracking error by sending tracking feedback information to the telescope control system. For efficient development and maintenance of each software package, we have applied software engineering methods, i.e., a spiral software development with model-based design. It is not trivial to define the shape and center of astronomical object point spread functions (PSFs), which do not have symmetric Gaussian profiles in short exposure (<4 s) guiding images. Efforts to determine the PSF centroid are additionally complicated by the core saturation of bright guide stars. We have applied both a two-dimensional Gaussian fitting algorithm (2DGA) and center balancing algorithm (CBA) to identify an appropriate method for IGRINS in the near-infrared K-band. The CBA derives the expected center position along the slit-width by referencing the spillover flux ratio of the PSF wings on both sides of the slit. In this research, we have compared the accuracy and reliability of the CBA to the 2DGA by using data from IGRINS commissioning observations at McDonald Observatory. We find that the performance of each algorithm depends on the brightness of the targets and the seeing conditions, with the CBA performing better in typical observing scenarios. The algorithms and test results we present can be utilized with future spectroscopic slit observations in various observing conditions and for a variety of spectrograph designs.

Evaluation of the implementation of a subset of ISO/IEC 29110 Software Implementation process in four teams of undergraduate students of Ecuador. An empirical software engineering experiment

The competitiveness of software development companies depends on their ability to offer software products with quality attributes within approved budget and schedule. Most Very Small Entities (VSEs) that develop software do not see the benefits of implementing software standards. Consequently, they limit their potential to be recognised as quality software development entities. In this study, the authors present results obtained through the application of empirical software engineering in an experiment in which the ISO/IEC TR 29110–5–1–2 “Software engineering – Lifecycle profiles for Very Small Entities (VSEs) – Part 5–1–2: Management and engineering guide: Generic profile group: Basic profile” was used. The guide includes two processes: Project Management (PM) process and Software Implementation (SI) process. The objective of the project was the development of a software product for the scheduling of medical appointments for the Student Wellness Center of a university of Ecuador. Four teams of undergraduate students were involved. Two of them (controlled teams) implemented a subset of the SI process, while the other two (non-controlled teams) had freedom to choose development activities that were subsequently mapped with the activities of the standard. All teams developed the software product using the SCRUM framework within the same timeframe. Although the experiment was focused on the SI process, the teams also used a tailored version of the PM process defined by the professors. The experiment execution encountered several difficulties. For example, the timeframe of six weeks established in the design of the experiment was too short since students worked part time in the project. All the teams experienced this difficulty, especially when they had to construct and test the software components. Overall, the teams that used the ISO/IEC TR 29110–5–1–2 guide achieved better scores in the quality evaluation of their software processes.

Special section on testing and repair for software engineering technologies and applications

Special section on testing and repair for software engineering technologies and applications

Internal Adaption based Nature Inspired Algorithm for Application in Software Engineering

In this paper, new mutation strategies are proposed to improve the accuracy of the cost estimation by COCOMO's tuning parameters using the Internal adaption based mutation operator for differential evolution algorithm (IABMO Algorithm). The proposed method provides more promising solutions to take the lead evolution and helps DE abstain the circumstance of stability. The proposed algorithm applied software cost estimation and improve the performance of the initial phase for software engineering. This approach is used for precise prediction and reduces the error rate for the initial phase of software development phase projects. The software cost estimation based IABMO algorithm has been capable of a better for effort, MRE, MMRE, and prediction.

A Study of Call Graph Construction for JVM-Hosted Languages

Call graphs have many applications in software engineering, including bug-finding, security analysis, and code navigation in IDEs. However, the construction of call graphs requires significant investment in program analysis infrastructure. An increasing number of programming languages compile to the Java Virtual Machine (JVM), and program analysis frameworks such as WALA and SOOT support a broad range of program analysis algorithms by analyzing JVM bytecode. This approach has been shown to work well when applied to bytecode produced from Java code. In this paper, we show that it also works well for diverse other JVM-hosted languages: dynamically-typed functional Scheme, statically-typed object-oriented Scala, and polymorphic functional OCaml. Effectively, we get call graph construction for these languages for free, using existing analysis infrastructure for Java, with only minor challenges to soundness. This, in turn, suggests that bytecode-based analysis could serve as an implementation vehicle for bug-finding, security analysis, and IDE features for these languages. We present qualitative and quantitative analyses of the soundness and precision of call graphs constructed from JVM bytecodes for these languages, and also for Groovy, Clojure, Python, and Ruby. However, we also show that implementation details matter greatly. In particular, the JVM-hosted implementations of Groovy, Clojure, Python, and Ruby produce very unsound call graphs, due to the pervasive use of reflection, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">invokedynamic</monospace> instructions, and run-time code generation. Interestingly, the dynamic translation schemes employed by these languages, which result in unsound static call graphs, tend to be correlated with poor performance at run time.