scholarly journals Software Measurement and Defect Prediction with Depress Extensible Framework

2014 ◽  
Vol 39 (4) ◽  
pp. 249-270 ◽  
Author(s):  
Lech Madeyski ◽  
Marek Majchrzak
Keyword(s):  
Open Source ◽  
Data Science ◽  
Defect Prediction ◽  
Software Systems ◽  
Software Measurement ◽  
Software Defect ◽  
Industrial Projects ◽  
Extensible Software ◽  
Development State ◽  
User Friendly

Abstract Context. Software data collection precedes analysis which, in turn, requires data science related skills. Software defect prediction is hardly used in industrial projects as a quality assurance and cost reduction mean. Objectives. There are many studies and several tools which help in various data analysis tasks but there is still neither an open source tool nor standardized approach. Results. We developed Defect Prediction for software systems (DePress), which is an extensible software measurement, and data integration framework which can be used for prediction purposes (e.g. defect prediction, effort prediction) and software changes analysis (e.g. release notes, bug statistics, commits quality). DePress is based on the KNIME project and allows building workflows in a graphic, end-user friendly manner. Conclusions. We present main concepts, as well as the development state of the DePress framework. The results show that DePress can be used in Open Source, as well as in industrial project analysis.

scholarly journals Bottlenecks in Software Defect Prediction Implementation in Industrial Projects

2015 ◽  
Vol 40 (1) ◽  
pp. 17-33 ◽  
Author(s):  
Jarosław Hryszko ◽  
Lech Madeyski
Keyword(s):  
Software Development ◽  
Defect Prediction ◽  
Software Systems ◽  
Development Projects ◽  
Software Defect Prediction ◽  
Software Defect ◽  
University Of Technology ◽  
Industrial Projects ◽  
Industrial Software ◽  
Extensible Software

Abstract Case studies focused on software defect prediction in real, industrial software development projects are extremely rare. We report on dedicated R&D project established in cooperation between Wroclaw University of Technology and one of the leading automotive software development companies to research possibilities of introduction of software defect prediction using an open source, extensible software measurement and defect prediction framework called DePress (Defect Prediction in Software Systems) the authors are involved in. In the first stage of the R&D project, we verified what kind of problems can be encountered. This work summarizes results of that phase.

A Novel Effort Measure Method for Effort-Aware Just-in-Time Software Defect Prediction

2021 ◽  
Vol 31 (08) ◽  
pp. 1145-1169
Author(s):  
Liqiong Chen ◽  
Shilong Song ◽  
Can Wang
Keyword(s):  
Prediction Model ◽  
Defect Prediction ◽  
Software Systems ◽  
Support Vector ◽  
Just In Time ◽  
Software Defect Prediction ◽  
Fine Grained ◽  
Software Defect ◽  
Code Changes ◽  
The Cost

Just-in-time software defect prediction (JIT-SDP) is a fine-grained software defect prediction technology, which aims to identify the defective code changes in software systems. Effort-aware software defect prediction is a software defect prediction technology that takes into consideration the cost of code inspection, which can find more defective code changes in limited test resources. The traditional effort-aware defect prediction model mainly measures the effort based on the number of lines of code (LOC) and rarely considers additional factors. This paper proposes a novel effort measure method called Multi-Metric Joint Calculation (MMJC). When measuring the effort, MMJC takes into account not only LOC, but also the distribution of modified code across different files (Entropy), the number of developers that changed the files (NDEV) and the developer experience (EXP). In the simulation experiment, MMJC is combined with Linear Regression, Decision Tree, Random Forest, LightGBM, Support Vector Machine and Neural Network, respectively, to build the software defect prediction model. Several comparative experiments are conducted between the models based on MMJC and baseline models. The results show that indicators ACC and [Formula: see text] of the models based on MMJC are improved by 35.3% and 15.9% on average in the three verification scenarios, respectively, compared with the baseline models.

Software defect prediction: A multi-criteria decision-making approach

2020 ◽  
Vol 15 (1) ◽  
pp. 35-42
Author(s):  
A.O. Balogun ◽  
A.O. Bajeh ◽  
H.A. Mojeed ◽  
A.G. Akintola
Keyword(s):  
Machine Learning ◽  
Software Testing ◽  
Evaluation Metrics ◽  
Defect Prediction ◽  
Software Systems ◽  
Software Defect Prediction ◽  
Learning Models ◽  
Decision Method ◽  
Software Defect ◽  
Machine Learning Models

Failure of software systems as a result of software testing is very much rampant as modern software systems are large and complex. Software testing which is an integral part of the software development life cycle (SDLC), consumes both human and capital resources. As such, software defect prediction (SDP) mechanisms are deployed to strengthen the software testing phase in SDLC by predicting defect prone modules or components in software systems. Machine learning models are used for developing the SDP models with great successes achieved. Moreover, some studies have highlighted that a combination of machine learning models as a form of an ensemble is better than single SDP models in terms of prediction accuracy. However, the efficiency of machine learning models can change with diverse predictive evaluation metrics. Thus, more studies are needed to establish the effectiveness of ensemble SDP models over single SDP models. This study proposes the deployment of Multi-Criteria Decision Method (MCDM) techniques to rank machine learning models. Analytic Network Process (ANP) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) which are types of MCDM techniques are deployed on 9 machine learning models with 11 performance evaluation metrics and 11 software defects datasets. The experimental results showed that ensemble SDP models are best appropriate SDP models as Boosted SMO and Boosted PART ranked highest for each of the MCDM techniques. Besides, the experimental results also validated the stand of not considering accuracy as the only performance evaluation metrics for SDP models. Conclusively, more performance metrics other than predictive accuracy should be considered when ranking and evaluating machine learning models. Keywords: Ensemble; Multi-Criteria Decision Method; Software Defect Prediction

AI-Based Software Defect Predictors: Applications and Benefits in a Case Study

AI Magazine ◽  
2011 ◽  
Vol 32 (2) ◽  
pp. 57 ◽  
Author(s):  
Ayse Tosun Misirli ◽  
Ayse Bener ◽  
Resat Kale
Keyword(s):  
Prediction Model ◽  
Software Metrics ◽  
Real Life ◽  
Pilot Project ◽  
Defect Prediction ◽  
Software Systems ◽  
Software Defect ◽  
Process Implementation ◽  
New Process ◽  
One Year

Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time and reduces the testing effort. We have built a learning-based defect prediction model for a telecommunication company in the space of one year. In this study, we have briefly explained our model, presented its pay-off and described how we have implemented the model in the company. Furthermore, we compared the performance of our model with that of another testing strategy applied in a pilot project that implemented a new process called Team Software Process (TSP). Our results show that defect predictors can predict 87 percent of code defects, decrease inspection efforts by 72 percent and hence, reduces post-release defects by 44 percent. Furthermore, they can be used as complementary tools for a new process implementation whose effects on testing activities are limited.

LEARNING BETTER INSPECTION OPTIMIZATION POLICIES

2012 ◽  
Vol 22 (05) ◽  
pp. 621-644 ◽  
Author(s):  
MARKUS LUMPE ◽  
RAJESH VASA ◽  
TIM MENZIES ◽  
REBECCA RUSH ◽  
BURAK TURHAN
Keyword(s):  
Open Source ◽  
Upper Bound ◽  
Defect Prediction ◽  
Software Systems ◽  
Social Analysis ◽  
Design And Implementation ◽  
Social Metrics ◽  
Activity Measures ◽  
Code Metrics

Recent research has shown the value of social metrics for defect prediction. Yet many repositories lack the information required for a social analysis. So, what other means exist to infer how developers interact around their code? One option is static code metrics that have already demonstrated their usefulness in analyzing change in evolving software systems. But do they also help in defect prediction? To address this question we selected a set of static code metrics to determine what classes are most "active" (i.e., the classes where the developers spend much time interacting with each other's design and implementation decisions) in 33 open-source Java systems that lack details about individual developers. In particular, we assessed the merit of these activity-centric measures in the context of "inspection optimization" — a technique that allows for reading the fewest lines of code in order to find the most defects. For the task of inspection optimization these activity measures perform as well as (usually, within 4%) a theoretical upper bound on the performance of any set of measures. As a result, we argue that activity-centric static code metrics are an excellent predictor for defects.

scholarly journals HASPO: Harmony Search-Based Parameter Optimization for Just-in-Time Software Defect Prediction in Maritime Software

2021 ◽  
Vol 11 (5) ◽  
pp. 2002
Author(s):  
Jonggu Kang ◽  
Sunjae Kwon ◽  
Duksan Ryu ◽  
Jongmoon Baik
Keyword(s):  
Open Source ◽  
Parameter Optimization ◽  
Open Source Software ◽  
Harmony Search ◽  
Class Imbalance ◽  
Defect Prediction ◽  
Just In Time ◽  
Software Defect Prediction ◽  
Software Defect

Software is playing the most important role in recent vehicle innovations, and consequently the amount of software has rapidly grown in recent decades. The safety-critical nature of ships, one sort of vehicle, makes software quality assurance (SQA) a fundamental prerequisite. Just-in-time software defect prediction (JIT-SDP) aims to conduct software defect prediction (SDP) on commit-level code changes to achieve effective SQA resource allocation. The first case study of SDP in the maritime domain reported feasible prediction performance. However, we still consider that the prediction model has room for improvement since the parameters of the model are not optimized yet. Harmony search (HS) is a widely used music-inspired meta-heuristic optimization algorithm. In this article, we demonstrated that JIT-SDP can produce better performance of prediction by applying HS-based parameter optimization with balanced fitness value. Using two real-world datasets from the maritime software project, we obtained an optimized model that meets the performance criterion beyond the baseline of a previous case study throughout various defect to non-defect class imbalance ratio of datasets. Experiments with open source software also showed better recall for all datasets despite the fact that we considered balance as a performance index. HS-based parameter optimized JIT-SDP can be applied to the maritime domain software with a high class imbalance ratio. Finally, we expect that our research can be extended to improve the performance of JIT-SDP not only in maritime domain software but also in open source software.

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