Mustafa Hammad

An approach to automatically enforce object-oriented constraints

This paper presents an approach to automatically enforce object-oriented constraints during incremental C++ code changes. The approach is realised as a tool to keep track on code changes and to notify developers with violations of predefined OO constraints. The OO constraints under consideration are taken from object-oriented design metrics. The object-oriented metrics mainly cover class size, coupling between classes, number of subclasses and inheritance tree. The goal of this work is to help designers to monitor design during incremental code changes. Object-oriented design metrics are automatically extracted from source code. The extracted metrics are used by designers to define the constraints. The tool supports defining and managing these OO constraints. After a code change is committed, design changes are identified and predefined constraints are checked for possible violations. The evaluation of the tool shows that it helps in detecting violations of design constraints, and it saves time and efforts of developers.

Sentiment Analysis for Arabic Reviews in Social Networks Using Machine Learning

In this emerging age of social media, social networks become growing resources of user-generated material on the internet. These types of information resources, which are an expansive platform of humans’ emotions, opinions, feedback, and reviews, are considered powerful informants for big industries, markets, news, and many more. The great importance of these platforms, in conjunction with the increasingly high number of users generating contents in Arabic language, makes maiming the Arabic reviews in social networks necessary. This paper applies four automatic classification techniques; these techniques are Support vector Machine (SVM) and Back-Propagation Neural Networks (BPNN), Naive Bayes, and Decision Tree. The main goal of this paper is to find a lightweight sentiment analysis approach for social networks’ reviews written in Arabic language. Results show that the SVM classifier achieves the highest accuracy rate, with 96.06% compared with other classifiers.

Identifying related commits from software repositories

Source code modifications are saved in software repositories as individual and independent commits. A high-level programming task is usually applied by related or similar code changes activities. This paper presents an approach to automatically identify related and similar source code modifications from software repositories. Discovering related commits helps maintainers to understand and trace the implementation of a specific programming task. Furthermore, identifying commits of a programming task leads to simplify code fixing and debugging activities. The identification is based on discovering relations among commits from software repositories. A relation is exposed based on the textual similarity between commits. Therefore, commits relationships lead to categorise commits into disjoint groups. Each generated group would represent related or similar code modifications activities. A group can be a set of maintenance tasks related to a specific feature in the system. A case study on an open source project is presented to investigate the proposed approach.

Generating Sequence Diagram and Call Graph Using Source Code Instrumentation

Understanding the dynamic behavior of the source code is an important key in software comprehension and maintenance. This paper presents a reverse engineering approach to build UML sequence diagram and call graph by monitoring the program execution. The generated models show the dynamic behavior of a set of target methods with time and object creation information. Timing and dynamic behavior details are extracted by instrumenting the target code with a set of calls to a monitoring function in specific instrumentation points in the source code. The proposed approach is applied on a case study to show the effectiveness and the benefits of the generated models.

Software Bug Prediction using Machine Learning Approach

Software Bug Prediction (SBP) is an important issue in software development and maintenance processes, which concerns with the overall of software successes. This is because predicting the software faults in earlier phase improves the software quality, reliability, efficiency and reduces the software cost. However, developing robust bug prediction model is a challenging task and many techniques have been proposed in the literature. This paper presents a software bug prediction model based on machine learning (ML) algorithms. Three supervised ML algorithms have been used to predict future software faults based on historical data. These classifiers are Naive Bayes (NB), Decision Tree (DT) and Artificial Neural Networks (ANNs). The evaluation process showed that ML algorithms can be used effectively with high accuracy rate. Furthermore, a comparison measure is applied to compare the proposed prediction model with other approaches. The collected results showed that the ML approach has a better performance.