Software defect prediction using machine learning

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 A software or system defect is a fault, a bug, a mistake, or a shortcoming in a computer program or software that makes it return surprising or off-base outcomes. The existence of software bugs legitimately influences the quality and maintenance cost of software frameworks. Software flaws or defects are programming errors that result in different results than anticipated. The majority of the errors are caused by source code or design, but some are caused by incorrect code produced by compilers can be identified by software defect prediction using machine learning.

Software defect prediction using machine learning

Software defects are a serious concern for both software developers and customers. Software defects not only reduce software quality and expense but also cause delays in the development process. To resolve this problem, software defect prediction (SoDP) is suggested. SoDP will effectively advance the efficacy of software testing and direct resource allocation. Software bugs/defects must be detected and fixed early in the SDLC to produce high-quality software.

Software defect prediction using machine learning

To predict the software defect/bug event and to understand the process of software development life cycle (SDLC) are major parts or components that prompt achievement standards. Imperfection forecast utilizing Machine learning calculation could be implemented in any phase of SDLC, for example, recognizable feasibility study, analysis, structure, coding, verification, and sustainability, and any kind of SDLC model, for example, Iterative Model, Waterfall Model, Spiral Model, Agile Model, V model, and Big Bang Model. Machine learning calculation and measurable investigation assist with speculating that a code is buggy [Machine Learning Introduction].

In terms of Institute of Electrical and Electronics Engineers (IEEE) standard 104, software defects are classified in various ways, which are:

Error

This can happen because the consumer is aware of the software’s flaw, which causes incorrect performance.

Fault

There has been an apparent mistake in the software products.

Failure

Stop the software products from performing their previous necessary functions, or incorrect results will be stored for each customer feedback.

Defect

The inability to carry out the system and tasks requirements and specifications specified by the customers and developers.

Bug

A software bug is a mistake or deficiency in the source code of a program or software. It is detected by either developers or testing team members.

Types of Software Defects

1. Requirement defects: These kinds of defects occur when incorrect requirements are established. The best ways to detect such types of defects are by inspection. Testing can prove to be costly because developing a system on a bad set of requirements and then when it fails, having to re-develop it.
2. Design defects: These defects occur when the system is improperly designed. Numerous empirical studies have committed that analyses were much more accurate and efficient than testing. A flaw discovered during design inspection is reasonable to correct than one find out during feature testing because the cost of rework in the latter is significantly higher.
3. Code defects: For these types of defects, functional or structural testing is found to be better than inspection. According to some research, testing and inspection find various types of code defects and can thus be used in tandem to complement each other.

Software Defect Prediction

In the area of software engineering, software defect prediction (SoDP) is a significant research area. The method of identifying faulty modules in software is known as software defect prediction or SoDP. SoDP advantages to optimize testing resource distribution by identifying defect-prone modules aforementioned to testing. Until deploying software systems, most companies want to estimate the number of bugs or recognize defect-prone modules. When a software is exposed to operation or during research, a diversity of statistical approaches and artificial intelligence (AI) techniques have been employed for defects prediction [impact of ai in business].

The following important areas during software development activities for prediction of software defects:

• Predict the software defect in software systems
• Improve the software quality
• Decrease the maintenance cost
• Decrease the efforts

The basic structure of the SoDP model

Software defect prediction is the most popular research area in software engineering. Software defect prediction works based on historical data [What is data science]. To construct machine learning classifiers to predict faulty code snippets, software defect prediction is a method of using historical data from software archives/ repositories such as code complexity and change records to create software defect metrics [unsupervised machine learning].

Process of software defect prediction model

Software defect prediction using machine learning

The following steps for the process of software defect prediction model are:

• Step 1: The initial step in creating a prediction model is to gather data from software achieve such as e-mail collections, version control systems, and problem tracking systems.
• Step 2: In keeping with prediction granularity, each one instance may characterize a device, a source code file, software component, a function, a class, and a code change.
• Step 3: In this situation, many metrics (or features) are extracted from program repositories and are labeled as buggy/clean or the number of bugs.
• Step 4: Apply pre-processing techniques after producing instances with metrics and labels.
• Step 5: The final step is to train a prediction model that can guess whether or not a new instance contains a bug.

Brief History of Software Defect Prediction Studies

In the year 1971, the author (Akiyama) performed the first research to estimate the number of defects/faults. The author created a simple model using LOC (Lines of Code) to reflect the complexity of software systems, based on four assumptions that complex source code might reason bugs/defects. However, LOC is an overly simplistic metric for illustrating machine complexity. In 1976 and 1977, MaCabe and Halstead introduced the cyclomatic complexity metric and the Halstead complexity metric, respectively. In the 1970s and early 1980s, these metrics were widely popular to develop models for appraising defects.

The models examined during that time were not prediction models, but rather fitting models that looked at the relationship between metrics and the number of defects [machine learning text recognition]. The authors developed a linear regression model and tested it on a different set of program code. Munson et al., on the other hand, argued that at the time, advanced regression techniques were not defined, and suggested a classification model that divides modules into two classes: low risk and high risk.

Researchers suggested various object-oriented (OO) metrics in 1994, to predict defects in object-oriented systems.

Defect/Bug Life Cycle

Predicting the existence of a defect and recognizing the life-cycle of defects are most important and compulsory, indicating the significance of predicting the defect/bug prior in the SDLC. Defect life cycle saves time, effort, and money in finding and repairing the defects during the software development. The number of defects/bugs posed throughout the software development cycle is one of the major issues confronting the modern software company/industry. This results in a delay in the product’s final delivery and an overall rise in the running cost. The model attempts to forecast the existence of the bug by using parameters from different dimensions. Most well-known software development models, such as waterfall, agile, V-shape, and spiral, are well-suited to the model.

One of the most important thing is to comprehend the defect management life cycle. Figure 3 presents the various states of defect life cycle which are as follows:

1. New: A new defect is raised for the first time by testers or someone else a portion of the SDLC.
2. Assigned: Each new bug is allocated to any one of the team members for instant determination, depending on the bug’s priority and seniority, and the equivalent developer will take action.
3. Open: If the preliminary state of the developer’s testing reveals that it is indeed a bug, it will be moved to the open state. A developer team or a product testing team will usually handle this.
4. Fixed: All open bugs should be patched according to their severity and priority. Throughout the software development life cycle, all stakeholders collaborate to determine the importance and severity of bugs.
5. Retest: If the bug isn’t checked or the explanation isn’t accurate, it will require further testing to ensure that the reported bug is correct in all situations.
6. Reopened: The reopened bugs are important because the amount of reopened bugs increases the service time. It takes longer to patch reopened bugs.
7. Rejected: Rejected bugs aren’t even bugs, but they were brought up by the testing team member. These are largely attributable to an ability set mismatch and specifications miscommunication.
8. Deferred: Some of the vulnerabilities will be pushed to the next updates due to priorities. Differentiated bugs are the classification for these bugs.
9. Duplicated: When the same bug occurs two or more times, we refer to it as a repeated bug.
10. Closed: This would be viewed as fixed bugs if the problem is correctly fixed.

Defect/bug life cycle

In this post, the introduction section discussed the process of software defect prediction in different phases/steps wise. Then we will explain the various ML categories (e.g., Supervised, Unsupervised or Semi-supervised Learning) to be used to predict the defect in a software. After that, will discuss various approaches (WPDP, CPDP, JITDP) are applied to software defect prediction. Then, we will move on to various measurement methods applied for predicting the software defects.

Different categories of machine learning

Different categories of Machine Learning

Supervised Learning

Where a model must be trained to make a prediction, supervised learning is used. Regression and classification are two different methods of supervised learning. To construct the prediction model, Machine learning classification (supervised) algorithms can be used with previous software metrics and fault marks. Concept learning, classification, rule learning, bayesian learning, LR, NN, and SVM are some of the most popular supervised machine learning approaches [supervised machine learning].

For bug prediction, researchers used a variety of deep learning algorithm, e.g., Linear regression, RF, NN, SVM, and DT. Standard defect prediction datasets were used to test the performance of these methods [components of data science]. In the term of bug prediction, the SVM approach outperformed all other methods.

To detect the software defects, researchers proposed a new classification model which is called Superposed Naive Bayes (SNB). The different classification models along with SNB were simulated on the 13 real-world public datasets and found that SNB achieved a fine balance between accuracy and interpretability.

Framework for SoDP via previous databases

Researchers reported RF and performed better than various machine learning algorithms. Adequate priorities were not provided on the selection of variables, transformations of variables, variable reductions, and feature engineering that could enhance the efficiency of the models. Various algorithms are currently available which could be practiced to predict the bug’s existence. Because of the ensemble nature, the random forest algorithm is the favorite option.

Researchers were using a novel benchmark method to predict and test software defects. During the assessment point, various learning schemes are assessed based on the system chosen [deep learning recommender system]. The most excellent learning scheme is then used to create a predictor with all historical/previous data in the prediction stage, and the analyst/developer is After that, it was used to predict a defect in new data.

Unsupervised Learning

When constructing unsupervised models using clustering algorithms, data sets have no class label (fault or non-fault) of each module. Unsupervised learning is a technique for detecting hidden patterns in input data. Clustering is a form of unsupervised learning. Sequential pattern mining, association rule mining, and clustering are the most popular unsupervised learning methods [Data science workflow].

Four variants of the K-mean clustering technique were considered in their study. Four real-world datasets have been used to evaluate the performance of different variants. According to the authors, the K-mean++ variant outperforms other K-mean variants.

The process of constructing an unsupervised learning model

For defect prediction problems, K-mean, X-mean, hierarchal clustering, density-based clustering, and Expectation minimization methods were studied. 

Due to the absence of historical data, researchers have used an unsupervised approach to predict software defects [what is big data?]. A signed Laplace spectral classifier was proposed to analyze defects. According to the simulation results, the suggested signed classifier outperformed the unsupervised technique significantly.

During the software development cycle, they developed a fuzzy-based approach for dealing with software defects. Twenty real-world datasets were used to test the suggested method. The proposed approach’s accuracy was found to be close to the real defect prediction rate.

Thet developed an artificial framework for extracting software defect fuzzy rules [artificial intelligence business]. The proposed model was capable to identify fault features. The model started with the assumption that every feature was a meaningless feature. The suggested framework’s performance was evaluated using publicly available software defect datasets. It was shaw that the suggested model could find fuzzy rules for software flaws.

Semi-Supervised Learning

Semi-supervised learning combines the benefits of both supervised and unsupervised learning. Semi-supervised learning is in the middle of the two types of learning: supervised and unsupervised. As compared to unsupervised learning, semi-supervised clustering increases efficiency, but unsupervised learning does not perform as compared to supervised learning. Semi-supervised learning is a form of ML that trains models using a combination of a lesser quantity of labeled data and a huge quantity of unlabeled data.

semi-supervised learning framework

The software engineering community has attempted to incorporate different machine learning methods such as active or semi-supervised learning for defect prediction as they have been suggested. researchers proposed defect prediction models based on active learning, in which a trial collection of occurrences is chosen and the occasions are queried by professionals [what is a sentiment analysis?].

A smaller number of modules were chosen and tested for the large software framework. A software defect prediction model was created to forecast defect proneness in the modules on the left. Random sampling with a semi-supervised trainer, traditional computer learners, and active sampling with an active semi-supervised learner are the three sample selection methods mentioned in that paper.

A semi-supervised organized dictionary learning (SSDL) technique was used. If there is an absence of historic data for constructing a reliable model, this method yields excellent results. They have derived with cross-project defect prediction and semi-supervised defect prediction (SSDP) as potential successful solutions. For cross software defect prediction and within-software defect prediction, the authors suggested an SSDL approach. They combined a minor quantity of labeled defect data with a major quantity of unlabeled data. SSDL performs better than related SSDP approaches, according to their findings. In the CSDP case, this occurs for two datasets.

Semi-supervised learning’s main objectives are to build a beginner that automatically unnecessary avails the large quantity of unlabeled data in addition to the small quantity of labeled data to help enhance learning results. Low-density separation based methods, Generative-model based methods, disagreement-based methods, and graph-based methods are the most popular semi-supervised learning methods.

Reinforcement Learning

Semi-supervised learning is not the same as reinforcement learning [Reinforcement Learning]. Reinforcement learning is a system in which reward values are associated with the various actions that the model is expected to take. Reinforcement learning includes a set of actions, parameters, and end values. It will train the machine by trial and error method. It will learn from past efforts to achieve the best possible result.

In Figure 7, the authors are described how interact an agent with an environment? How to improve or enhance the states? How to define the set of actions? Let us consider it as the state with S, action with A, and reward with R If. An agent completes an action and reaches the environment so that Ai will increase by 1, where i means 1, 2, 3, and so on. Similarly, the environment will give an acknowledgment in terms of reward as well as a state will increase with 1, so that set of the state is Si. As seen in Figure 7, this results in a series of states Si, action Ai, and immediate rewards Ri. The agent’s task is to figure out a control policy, n: S + A, that maximizes the expected amount of these rewards, with future rewards discounted exponentially by their delay.

A framework of Reinforcement Learning

Software defect prediction approaches

Within-Project Defect Prediction

The machine learning techniques used, as well as the consistency of the training data, influence a model’s predictive power. In most cases, defect prediction models are studied in the literature in a within-project sense, assuming that previous defect data for a specific project exists. Within-project defect prediction (WPDP) is the name of this process. WPDP is a prediction model that can be built by collecting historical data from a software project and predicts faults in that project. If there is a satisfactory quantity of historical data to train models, WPDP performed best.

Many new technologies and development methods will impact the new code submitted after the upgrade, and could even introduce new defects in software development, according to the within-project defect prediction. As a result, begin by sorting the data by time series, then divide the data from previous years into a category, and choose the data from the previous year as the training set and the data from the following year as the test set. That does not only ensure the integrity of a project development period but also that the amount of data in the training set for a few years is adequate to improve the accuracy of the with-in project defect prediction.

Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is the process of forecasting defects in other projects using prediction models trained on data from previous projects. When a project does not have enough historical data to train a model, CPDP is used in this mode. As a result, a prediction model is created for one project and then applied to another or several projects. The prediction models have been transferred from one project to another project.

The effect of a predictor in machine learning-based predictions is determined by 2 aspects: the learning algorithm and training data. As a result, there are two possible approaches to CPDP:

1. Identifying the most appropriate training data for the project that needs to be predicted. In an ideal world, we’d be able to find learning data with a similar flaws pattern as the goal project, resulting in satisfactory prediction performance.
2. To build the defect predictor, we used learning algorithms with a high generalization potential. This approach assumes that is a consistent defect pattern across all datasets. If it can effectively train this pattern, it can foresee flaws in another project.

Just-in-Time Defect Prediction

JITDP (Just-in-Time Defect Prediction) is a form of SoDP approach that allows software change-level predictions. It detects defect-causing program changes as soon as made (i.e., “just-in-time”). Inspection is much simpler at this point because the improvements are still new in the developers’ minds.

Advantages of JITDP over component level SoDP include:

1. Prediction is made early in the process, allowing for easier code inspection.
2. Predictions with a finer granularity, making it easier to see defects.
3. Developers are assigned to inspect the code straightforwardly.
4. Debugging code after a defect report has been produced.
5. SoDP, which focuses on predicting flaws in software components (e.g., files, packages, etc.), is a more traditional approach

In the active research areas of software engineering, Software Defect Prediction (SoDP) plays an important role in detecting software defects. This post helps to understand various software defects and SoDP model. Also a brief histroy of software defect prediction along with the defect life cycle of software is discussed. Due to the popularity of Machine learning (ML) techniques in field of software engineering they are used to predict software defects. Various ML categories such as Supervised, Unsupervised, Semi-supervised, and Reinforcement learning are applied on different defect prediction approaches such as with-in project, cross-project, and just-in-time project.