General
How Machine Learning Can Speed Up Regression Testing and Improve Accuracy
Modern software releases move fast, and manual regression testing often slows teams down. Machine learning helps automate repetitive checks, cut testing time, and reduce human error. It speeds up regression testing by predicting which test cases matter most and improves accuracy by detecting defects that manual testing can miss.
By analyzing past results and application changes, machine learning models identify high-risk areas that need attention. They can also update test scripts automatically and flag false positives before they waste valuable time. The process transforms quality assurance from a time-heavy step into an intelligent, data-driven practice.
As the discussion continues, the focus will shift to how specific machine learning techniques accelerate regression testing and how these models raise accuracy through smarter decision-making. Understanding these methods helps teams deliver better software faster and with greater confidence.
Machine Learning Approaches for Accelerating Regression Testing
Machine learning models now play a key role in improving test speed, accuracy, and adaptability. They help teams predict risk, focus on high-impact areas, and maintain large test suites with less manual input. Machine learning models now play a key role in improving test speed, accuracy, and adaptability. By analyzing past test data and application changes, these models help identify the most critical areas for testing, ensuring a more targeted approach. By prioritizing high-risk areas, machine learning-driven testing explained by Functionize allows teams to streamline their testing efforts, reducing unnecessary tests and focusing on what matters most. Compared to traditional manual testing, which can be time-consuming and prone to human error, machine learning models can quickly identify patterns and potential issues that might otherwise go unnoticed. While automated testing tools can speed up the process, machine learning-driven approaches offer a more intelligent, data-driven way to improve both speed and accuracy. As these models continuously learn from new data, they adapt to changes in the application, further refining their predictions.
Automated Test Case Prioritization and Selection
Automating test case selection saves time and helps quality teams focus on changes that matter most. Machine learning models analyze historical data such as past defects, code changes, and execution logs to determine which tests have the highest chance of catching new issues. This allows testers to run fewer but more meaningful tests.
Predictive algorithms can rank test cases by likelihood of failure or business impact. For instance, a model might use data on recent commits or modules with high defect density to reorder the suite. Teams then gain faster feedback without running every test after each build.
By pairing historical analytics with real-time signals, this approach reduces test redundancy while keeping accuracy high. It supports continuous delivery pipelines that require quick cycle times and minimal rework.
AI-Powered Test Suite Maintenance and Self-Healing Scripts
Maintenance creates major delays in large regression cycles. As interfaces or code structures evolve, older scripted tests often stop working. Machine learning can fix this problem by enabling self-healing test logic. It learns from element attributes, layout changes, and user flows to adjust tests automatically.
This approach reduces manual effort, since a tester does not need to rewrite scripts after each UI update. Modern tools track patterns across thousands of interface elements and use visual recognition to identify what changed in the application.
By adapting on its own, the system keeps tests useful through many software updates. The result is less downtime and fewer false failures, even as products shift between releases.
Optimizing Test Coverage and Efficiency with Predictive Analytics
Predictive analytics models find gaps in existing tests and highlight areas that may need more coverage. This process often uses code churn data, historical defect rates, and user interaction logs to show where defects are most likely to appear. Teams then direct testing resources to those higher-risk areas.
These analytics can also help balance test distribution. For example, low-risk components might need only lightweight checks, while high-impact modules receive deeper testing.
Applying predictive insights helps achieve both higher coverage and faster delivery. It also allows early detection of potential stability issues before they reach production, reducing overall costs and improving test efficiency across each release cycle.
Improving Regression Test Accuracy with Machine Learning Models
Machine learning models can increase regression test accuracy by predicting which test cases matter most, identifying fault patterns in code, and reducing the time needed to verify new changes. Effective use of data preparation, model selection, and evaluation leads to stronger predictions and fewer missed defects.
Model Selection and Evaluation for Test Prediction
Choosing the right regression model defines how well predictions match real test outcomes. Models such as linear regression, random forest, support vector regression, and gradient boosting each handle data relationships differently. A good approach is to begin with a baseline model to compare performance across several algorithms.
Teams often use scikit-learn tools like RandomForestRegressor, GradientBoostingRegressor, and Ridge to predict regression test results. Selecting models with strong generalization avoids wasted computing time. Cross-validation, especially k-fold cross-validation, helps confirm performance consistency across datasets.
Evaluation metrics such as mean squared error (MSE), root mean squared error (RMSE), and R² (coefficient of determination) measure predictive accuracy. Lower MSE or RMSE values indicate better alignment between predictions and test outcomes. Using multiple metrics gives a balanced view of model performance.
Feature Engineering and Data Preparation for Reliable Outputs
Accurate results depend on clean and well-prepared data. Exploratory data analysis (EDA) helps detect outliers, missing values, or strong correlations that may distort predictions. Columns can be transformed using feature scaling, standardization through StandardScaler, or one-hot encoding for categorical variables.
Data normalization keeps all features within a similar scale, which prevents large-value features from dominating the model. Missing values can be filled with methods like SimpleImputer, improving input consistency. Feature selection or recursive feature elimination (RFE) can remove unnecessary inputs that lower performance.
Balanced and properly formatted input data allows regression models to identify true patterns in software behavior. A clear data structure reduces noise in predictions and increases confidence in each result.
Preventing Overfitting and Ensuring Model Strength
Models that perform too well on training data may fail with new code changes. Overfitting often occurs when the model captures random noise instead of meaningful test patterns. Careful cross-validation and hyperparameter tuning help control this issue.
Regularization techniques such as Lasso regression and Ridge regression limit unnecessary complexity by applying penalties to large coefficients. This keeps predictions stable across updates. GridSearchCV in scikit-learn can systematically test combinations of regularization strengths to find balanced settings.
Ensemble methods like bagging, random forest, or gradient boosting (XGBoost) combine multiple predictors to increase stability. These approaches average out errors across models and reduce sensitivity to specific data conditions. A consistent evaluation process helps maintain long-term predictive accuracy in regression testing pipelines.
Conclusion
Machine learning allows teams to speed up regression testing by analyzing test results and predicting which areas of code need the most attention. This targeted approach cuts down on repetitive test runs and saves time. As a result, test cycles move faster without losing accuracy.
AI-based tools also make test scripts adapt automatically to software updates. That reduces manual maintenance and helps keep test cases relevant. By using data-driven insights, teams can focus on the most important test cases instead of running thousands that add little value.
The technology also improves defect detection. For example, algorithms can separate real failures from false positives, so testers can act on genuine issues more quickly. The process becomes more efficient and dependable.
In summary, machine learning makes regression testing faster, smarter, and more precise. It allows development teams to maintain software quality while releasing updates at a steady pace.
By Adedapo Adesanya
The Nigerian Electricity Regulatory Commission (NERC) has dissolved the board of Kaduna Electricity Distribution Plc over the company’s cumulative market obligations of N456.5billion and prolonged financial and operational challenges.
The regulator also appointed an interim board of special directors and directed the commencement of a transparent process for selecting a new core investor for the electricity distribution company.
The decisions were contained in Order No. NERC/2026/086, titled Order on the Regulatory Intervention in Kaduna Electricity Distribution Plc Pursuant to the Electricity Act 2023, which took effect on Monday, August 10, 2026.
NERC said the intervention followed an inquiry and consultations with key industry stakeholders, including the Bureau of Public Enterprises, and was necessitated by KAEDC’s prolonged regulatory and market defaults, inadequate investment and weak operational and commercial performance.
The commission said KAEDC’s cumulative market obligation since privatisation stood at approximately N456.5 billion as of May 2026, comprising N415.5 billion owed to the Nigerian Bulk Electricity Trading (NBET) Plc and N41 billion due to the Nigerian Independent System Operator (NISO)
The company also had other non-market statutory and third-party obligations amounting to N14.26billion, according to the regulator.
NERC said that since ASI Engineering Limited took over operations of KAEDC in June 2024, the company had accrued additional market debt of more than N118.6 billion as of May 2026.
The Commission described the company’s situation as grave, citing prolonged regulatory and market defaults, inadequate investment, weak operational and commercial performance, insufficient assets relative to liabilities and the absence of a credible pathway to sustainable recovery.
NERC said KAEDC paid only 41.93 per cent of its adjusted market invoices in 2025, resulting in a market shortfall of approximately N46.71bn during the year.
It linked the poor remittance performance to the company’s high aggregate technical, commercial and collection losses, which stood at 71.88 per cent in 2025.
The regulator explained that the losses meant KAEDC could account for only 28.2 per cent of the electricity received and delivered to end-use customers during the review period.
NERC also said ASI failed to meet its capital injection commitments towards recapitalising the utility.
According to the commission, KAEDC’s actual capital expenditure in 2025 was approximately N2.48 billion, against a minimum provision of N24.51 billion, representing only 10 per cent performance.
The regulator further noted that KAEDC’s meter coverage had remained between 33.26 per cent and 35.54 per cent since ASI took over the company, despite several interventions aimed at supporting meter deployment across distribution companies.
NERC said the company’s financial difficulties persisted despite approximately N6.58billion in regulatory derogations granted between January 2024 and May 2026 and aggregate Federal Government intervention disbursements of approximately N53.79 billion since July 2018.
It warned that the continued underperformance posed a material risk to electricity consumers, creditors, market stability and the continuity of electricity services.
NERC said it had previously notified KAEDC’s major shareholders and Afreximbank of the imminent intervention and required them to present a credible plan to address the company’s financial situation.
Representatives of ASI, NERC, BPE, Afreximbank and Fidelity Bank subsequently met on June 11, 2026, to discuss proposals for rescuing the company.
According to the commission, the parties agreed that ASI had not complied with conditions prescribed for its acquisition of a 60 per cent majority shareholding in KAEDC and had also failed to comply with BPE requirements for finalising the shareholding arrangements.
NERC said ASI subsequently requested an extension of up to 24 months to stabilise KAEDC’s cash flow, prioritise critical investments and deliver measurable performance improvements, including a pathway to full market remittance.
The regulator, however, rejected the request, saying ASI had been in effective control of KAEDC since June 2024 without a corresponding improvement in its financial and operational performance.
NERC subsequently resolved to exercise its powers under Sections 75 to 79 of the Electricity Act 2023 to dissolve the KAEDC board, preserve the company as a going concern and facilitate a transparent transition to a credible core investor within 12 months.
Consequently, the commission ordered the dissolution of KAEDC’s board and removal of all its directors from office.
“KAEDC’s board of directors is HEREBY DISSOLVED. All directors of KAEDC are removed from office, and the existing board stands dissolved pursuant to section 75 of the EA,” the order stated.
NERC appointed seven special directors to constitute the interim board for the transition period, with Dr Abdullahi Garba as chairman. Other members are Engineer Francis Agoha, Mr Aliyy Aliyu, retired Major General Henry Ayamasaowei, Dr Haliru Dikko, Mr Ayodeji Gbeleyi, representing the BPE, and Dr Abubakar Umar Hashidu.
The commission also appointed the incumbent Managing Director and Chief Executive Officer, Dr Abubakar Umar Hashidu, as administrator for an initial six-month term, subject to review.
NERC said the administrator would oversee the company’s day-to-day operations, ensure continuity of electricity services, implement interim board resolutions, comply with regulatory directives and safeguard the company’s assets and records.
The commission also withdrew the Know-Your-Licensee approvals issued to members of KAEDC’s management team and directed affected management staff to present themselves for revalidation.
Meanwhile, NERC directed Afreximbank to coordinate an open, competitive and transparent process for securing a replacement core investor for KAEDC.
The preferred investor is to be presented to NERC for approval, with the process expected to be completed within 12 months from the commencement of the order, unless the commission grants a written extension.
By Adedapo Adesanya
The federal government has unveiled the Tinubu Light Initiative, a presidency-backed renewable energy programme designed to provide affordable clean electricity to one million Micro, Small and Medium Enterprises across Nigeria.
The initiative, unveiled by the National Board for Technology Incubation during the National Showcase of the NextGen Innovation Challenge 2026 in Abuja, is also expected to create more than 50,000 direct jobs while supporting local manufacturing and accelerating the adoption of renewable energy.
The programme is targeted at reducing the high cost of energy that continues to constrain businesses, particularly MSMEs that rely heavily on petrol and diesel generators amid persistent gaps in grid electricity supply.
Speaking at the event, the Director-General and Chief Executive Officer of the NBTI, Mr Kazeem Raji, said the initiative was developed in response to the growing energy burden faced by Nigerian businesses.
Mr Raji said the Tinubu Light Initiative would deploy innovative financing models, strategic partnerships and renewable energy technologies to provide cleaner and more affordable electricity to MSMEs nationwide.
“The Tinubu Light Initiative seeks to change this narrative. Through innovative financing models, strategic partnerships, renewable energy technologies and nationwide implementation, this initiative will provide affordable clean energy solutions to one million Nigerian MSMEs,” he said.
According to him, lowering the energy costs of one million businesses would enable them to redirect resources towards expansion, investment and job creation, while strengthening the competitiveness of locally produced goods.
Mr Raji said the initiative would also go beyond electricity access by supporting the local assembly and production of renewable energy equipment, reducing carbon emissions and expanding access to digital financing, with particular opportunities for women and young entrepreneurs.
“This initiative goes beyond electrification. It is an industrial policy. It is an employment strategy. It is a poverty reduction programme. It is a climate action initiative. It is a national productivity agenda,” he said.
The initiative comes against the backdrop of rising energy costs for Nigerian businesses, with many MSMEs increasingly dependent on self-generation to sustain operations. The cost of petrol and diesel used to power generators has become a significant component of operating expenses, limiting production capacity and putting pressure on jobs.
Mr Raji said the Tinubu Light Initiative was aligned with the Federal Government’s broader economic strategy of leveraging technology, innovation and entrepreneurship to boost domestic production and create sustainable employment.
At the event, he also highlighted the NextGen Innovation Challenge, which attracted thousands of applications from innovators across sectors including renewable energy, agriculture, artificial intelligence, biotechnology, healthcare, manufacturing, education, fintech, climate technology and industrial engineering.
He said the challenge was increasingly becoming a platform for connecting Nigerian innovators with investors and supporting the transition of promising technologies from research and development to commercial applications.
Mr Raji disclosed that an innovator who participated in the inaugural 2025 edition secured a £1.5 million investment commitment, while agricultural technologies developed through the programme are being deployed in Kaduna, Bauchi and other states to improve productivity and reduce post-harvest losses.
He said the NBTI would continue to leverage its network of Technology Incubation Centres to identify innovators, provide mentorship, facilitate technology transfer and support the commercialisation of indigenous technologies.
Mr Raji further announced that the NextGen Innovation Challenge had secured the support of the Commonwealth Secretariat, which would enable the programme to expand beyond Nigeria into a Commonwealth-wide initiative involving all 56 member countries.
By Adedapo Adesanya
The Socio-Economic Rights and Accountability Project (SERAP) has urged all 19 presidential candidates announced by the Independent National Electoral Commission (INEC) to publish details of their assets and liabilities ahead of the 2027 elections.
The group also urged the candidates’ spouses, and where applicable, their unmarried children under 18, to do the same.
It further advised the candidates to disclose the legitimate sources of their significant assets and publicly reject vote-buying and electoral bribery before and during the election.
The organisation called on the candidates to instruct their parties, campaign organisations, agents and supporters not to offer or distribute money, gifts or other material inducements in exchange for votes.
The presidential candidates are President Bola Tinubu (APC), Mr Atiku Abubakar (ADC), Mr Peter Obi (NDC), Senator Sandy Onor (PDP), Mr Omoyele Sowore (AAC), Mr Donald Duke (PRP), Mrs Okwori Ada Elizabeth Frederick (NDP), Mr Chukwu Anita Zugwai (YPP), Mr Rufai Adekunle Omoaje (AA), and Mr Adenuga Sunday (Boot Party).
Others are Mr Memeh Samuel (DLA), Mr Nwanyanwu Daniel Danerechukwu (ZLP), Mr Okereke Sunday Chibuzor (LP), Mr Okereke Iken Esther (NRM), Mr Abbas-Bin Aliyu (ADP), Mr Dikwa Suleiman Mohammed (NNPP), Mr Adebayo Adewole Ebenezer (SDP), Mr Seyi Makinde (APM), and Mr Yusuf Kabiru (APP).
In an open letter to the candidates dated August 8, 2026, and signed by SERAP Deputy Director Kolawole Oluwadare, the organisation urged them to “go beyond the bare legal minimum and voluntarily embrace higher standards of transparency, accountability and integrity in seeking Nigeria’s highest elected office.”
SERAP said candidates seeking Nigerians’ mandate to exercise constitutional powers over public finances, natural resources, appointments and security institutions should be willing to subject their personal financial affairs to reasonable public scrutiny before asking for votes.
“Nigerians should not be asked to choose between candidates on the basis of who can spend the most money. They should be able to choose on the basis of policies, competence, integrity, character and their vision for Nigeria,” the organisation said.
SERAP said voluntary pre-election disclosure would enable voters to assess potential conflicts of interest and significant sources of wealth, strengthen public confidence in the electoral process and provide a baseline against which future changes in assets could be assessed if a candidate is elected.
“The 2027 presidential election presents an opportunity for political leaders to show that public office is a public trust. Candidates who voluntarily disclose their assets and reject vote-buying can show that they are prepared to uphold the transparency and accountability they promise to deliver if elected,” it said.
The organisation also cited constitutional and international provisions in support of its call, noting that although the 1999 Constitution, as amended, does not expressly require presidential candidates to publish their asset declarations before an election, it embodies principles of transparency, accountability, integrity in public office and meaningful participation in government.
SERAP noted that the Constitution already requires elected public officers, including the President, to declare their assets and liabilities.
It cited Paragraph 11 of Part I of the Fifth Schedule, which requires public officers to submit declarations of their properties, assets and liabilities, including those of unmarried children under 18, as well as Section 140(1), which requires a person elected President to make the prescribed declaration before assuming the functions of office.
On vote-buying, the organisation said the persistent use of money, gifts and other inducements to influence voters was a major threat to electoral integrity.
“We are also concerned about the persistent use of money, gifts and other inducements to influence voters. Vote-buying directly undermines the constitutional principle that sovereignty belongs to the people,” it said.
SERAP cited Section 14(2)(a) of the Constitution, which provides that sovereignty belongs to the people of Nigeria, as well as Section 125 of the Electoral Act 2026, which it said criminalises bribery and related conduct intended to procure the return of a person to elective office or the vote of an elector.
It added that vote-buying was particularly harmful amid poverty and economic hardship because it exploits economic vulnerability and risks turning a constitutional political right into a financial transaction.
It, therefore, urged the 19 presidential candidates to publish their assets and liabilities before the election, including relevant assets and liabilities of their spouses and unmarried children under 18, and disclose the legitimate sources of significant assets, including business interests, investments, real property, substantial gifts and inheritance, while protecting legitimate personal security and privacy.
The organisation also asked the candidates to commit to updating their public declarations if elected and explaining material increases in wealth; publicly reject vote-buying and electoral bribery; instruct their campaign organisations and political associates not to distribute money, gifts, food, transportation benefits or other material inducements in exchange for votes; report credible allegations of vote-buying involving their campaign organisations to the appropriate authorities; and sign and publish a public integrity pledge committing themselves, their parties and campaign organisations to peaceful, transparent, accountable and corruption-free elections.
“The choice before Nigerians in 2027 should be a choice based on ideas, policies, competence and integrity—not on who can spend the most money or conceal the most wealth,” it said.
