Research on the Application of Data Mining Technology in Software Engineering

With the development of computer science and software engineering, software systems are becoming larger and more complex in scale and function. How to eff ectively manage and utilize data during development, testing, and maintenance, improve software quality, reduce development costs, and increase productivity has become an important research topic in the fi eld of software engineering. As an eff ective data analysis method, data mining technology has been widely used in the fi eld of software engineering. Data mining technology can help software engineers mine useful information and knowledge from data, improve the quality and performance of software systems, reduce development costs, and accelerate the software development process. This article introduces the research status and development trend of applying data mining technology in software engineering. Firstly, it introduces the application scenarios and objectives of data mining in the fi eld of software engineering, including defect prediction, demand analysis, and software quality evaluation. It discusses the research hotspots and future development trends of data mining technology in software engineering, including deep learning, interpretable data mining, and cross domain data mining

Data Mining and Machine Learning for Software Engineering

Software engineering is one of the most utilizable research areas for data mining. Developers have attempted to improve software quality by mining and analyzing software data. In any phase of software development life cycle (SDLC), while huge amount of data is produced, some design, security, or software problems may occur. In the early phases of software development, analyzing software data helps to handle these problems and lead to more accurate and timely delivery of software projects. Various data mining and machine learning studies have been conducted to deal with software engineering tasks such as defect prediction, effort estimation, etc. This study shows the open issues and presents related solutions and recommendations in software engineering, applying data mining and machine learning techniques

Investigating Role of Data Mining in Software Engineering

  Companies that focus on software development produce vast volumes of data. Every stage of software development, from gathering requirements to ongoing upkeep, generates its own set of data. To better the software, efforts are undertaken to collect and store data produced in software repositories. Data mining techniques are used to the massive amounts of data found in software repositories in order to extract previously unseen patterns and insights. Researchers from the fields of Software Engineering and Data Mining have lately made this area of study a top priority. This research aims to examine the many uses of data mining in software engineering, the many types of software engineering data that can be mined, and the many data mining techniques that are available and have been used by researchers to solve the problems that this research focuses on. The next step is to use this classification to determine which subfield within software engineering has the highest scholarly interest.   &nbsp

Defect Prediction on the Hardware Repository - A Case Study on the OpenRISC1000 Project

Software defect prediction is one of the most active research topics in the area of mining software engineering data. The software engineering data sources like the code repositories and the bug databases contain rich information about software development history. Mining these data can guide software developers for future development activities and help managers to improve the development process. Nowadays, the computer-engineering field has rapidly evolved from 1972 until present times to the modern chip design, which looks superficially and very much like software design. Hence, the main objective of this thesis is to check whether it would be possible to apply software defect prediction techniques on hardware repositories. In this thesis, we have applied various data mining methods (e.g., linear regression, logistic regression, random forests, and entropy) to predict the post-release bugs of OpenRISC 1000 projects. We have conducted two types of studies: classification (predicting buggy and non-buggy files) and ranking (predicting the buggiest files). In particular, the classification studies show promising results with an average precision and recall of up to 74% and 70% for projects written in Verilog and close to 100% for projects written in C

Elementos para una ingeniería de explotación de información

Los Proyectos de Explotación de Información difieren sustancialmente de los pertenecientes al Software tradicional. Las fases clásicas de desarrollo le son ajenas, al igual que las herramientas involucradas en los procesos de Ingeniería en Software. Un nuevo cuerpo de conocimientos atento a las necesidades de su aplicación industrial, deviene, pues, imprescindible para el avance del nuevo campo disciplinar. En este artículo proponemos: un modelo de negocio, un proceso de educción de requisitos, un método de estimación, una metodología de selección de herramientas, un proceso de transformación de datos y una serie de procesos basados en técnicas de minería de datos.The Information Mining Projects have different characteristics compared to traditional software projects. The classic development phases do not apply to the natural phases of Information Mining Projects. Not all Software Engineering tools not apply to these projects. A new body of knowledge is necessary for Information Mining Engineering with a special focus on its use in industry. In this paper we propose: process model, requirement elicitation process, estimation method, a method for selecting the data mining tool, a methodology for transforming the data and, a set of processes for information mining based on the application of different data mining techniques

Data mining for software engineering and humans in the loop

The field of data mining for software engineering has been growing over the last decade. This field is concerned with the use of data mining to provide useful insights into how to improve software engineering processes and software itself, supporting decision-making. For that, data produced by software engineering processes and products during and after software development are used. Despite promising results, there is frequently a lack of discussion on the role of software engineering practitioners amidst the data mining approaches. This makes adoption of data mining by software engineering practitioners difficult. Moreover, the fact that experts’ knowledge is frequently ignored by data mining approaches, together with the lack of transparency of such approaches, can hinder the acceptability of data mining by software engineering practitioners. To overcome these problems, this position paper provides a discussion of the role of software engineering experts when adopting data mining approaches. It also argues that this role can be extended to increase experts’ involvement in the process of building data mining models. We believe that such extended involvement is not only likely to increase software engineers’ acceptability of the resulting models, but also improve the models themselves. We also provide some recommendations aimed at increasing the success of experts involvement and model acceptability

Privacy Preserving Mining in Code Profiling Data

Privacy preserving data mining is an important issue nowadays for data mining. Since various organizations and people are generating sensitive data or information these days. They don’t want to share their sensitive data however that data can be useful for data mining purpose. So, due to privacy preserving mining that data can be mined usefully without harming the privacy of that data. Privacy can be preserved by applying encryption on database which is to be mined because now the data is secure due to encryption. Code profiling is a field in software engineering where we can apply data mining to discover some knowledge so that it will be useful in future development of software. In this work we have applied privacy preserving mining in code profiling data such as software metrics of various codes. Results of data mining on actual and encrypted data are compared for accuracy. We have also analyzed the results of privacy preserving mining in code profiling data and found interesting results